
import pandas as pd
import numpy as np
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, confusion_matrix


df = pd.read_csv("stock_tweets.csv")
df.head()


df.dtypes

Date            object
Tweet           object
Stock Name      object
Company Name    object
dtype: object


df.shape

(80793, 4)


# Filter the dataframe for rows where the company is 'Tesla'
df = df[df['Company Name'] == 'Tesla, Inc.'].copy()
df


df.shape

(37422, 4)


# Convert "Published Date" to datetime format
df['Date'] = pd.to_datetime(df['Date'])
df.dtypes

Date            datetime64[ns, UTC]
Tweet                        object
Stock Name                   object
Company Name                 object
dtype: object


# Format the 'Date' column to include only the date part (yyyy-mm-dd)
df['Date'] = df['Date'].dt.strftime('%Y-%m-%d')
df.head()


df.shape

(37422, 4)


# Remove repeated rows
df = df.drop_duplicates()


df.shape

(37413, 4)


df.isnull().sum()

Date            0
Tweet           0
Stock Name      0
Company Name    0
dtype: int64


df.drop(['Stock Name', 'Company Name'], axis=1, inplace=True)
df.head()

<ipython-input-13-cba4765805bd>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df.drop(['Stock Name', 'Company Name'], axis=1, inplace=True)


import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!

True


import re
import matplotlib.pyplot as plt
from nltk.corpus import stopwords
from wordcloud import WordCloud
from collections import Counter
from nltk.tokenize import word_tokenize

# Function to plot frequent punctuation marks
def plot_punctuation_frequency(text, top_n=20):
    # Find all punctuation marks
    punctuation = re.findall(r'[^\w\s]', text)
    # Count the frequency of each punctuation mark
    punctuation_count = Counter(punctuation)
    # Get the most common punctuation marks
    most_common_punctuation = punctuation_count.most_common(top_n)
    # Unpack the keys and values for plotting
    keys, values = zip(*most_common_punctuation)
    # Plot the frequency
    plt.figure(figsize=(18, 6))
    plt.bar(keys, values)
    plt.xlabel('Punctuation Marks')
    plt.ylabel('Frequency')
    plt.title(f'Top {top_n} Most Frequent Punctuation Marks')
    plt.show()

# Function to plot frequent stop words
def plot_stopwords_frequency(text, top_n=20):
    # Tokenize the text
    tokens = word_tokenize(text)
    # Find stop words
    stop_words = set(stopwords.words('english'))
    stop_words_found = [word for word in tokens if word in stop_words]
    # Count the frequency of each stop word
    stop_words_count = Counter(stop_words_found)
    # Get the most common stop words
    most_common_stopwords = stop_words_count.most_common(top_n)
    # Unpack the keys and values for plotting
    keys, values = zip(*most_common_stopwords)
    # Plot the frequency
    plt.figure(figsize=(18, 6))
    plt.bar(keys, values)
    plt.xlabel('Stop Words')
    plt.ylabel('Frequency')
    plt.title(f'Top {top_n} Most Frequent Stop Words')
    plt.show()

# Function to generate word cloud
def generate_word_cloud(text):
    wordcloud = WordCloud(width=1200, height=600,
                          background_color='white',
                          stopwords=set(stopwords.words('english')),
                          min_font_size=10).generate(text)
    plt.figure(figsize=(8, 10), facecolor=None)
    plt.imshow(wordcloud)
    plt.axis("off")
    plt.tight_layout(pad=0)
    plt.show()


# Visualize punctuation frequency
text = ' '.join(df['Tweet'])
plot_punctuation_frequency(text)


# Visualize stop words frequency
plot_stopwords_frequency(text)


# Generate word cloud
generate_word_cloud(text)


# Function to clean tweets
def clean_tweet(tweet):
    # Remove mentioned words
    tweet = re.sub(r'@\w+', '', tweet)
    # Remove links
    tweet = re.sub(r'http\S+|www.\S+', '', tweet)
    # Remove extra spaces
    tweet = re.sub(r'\s+', ' ', tweet).strip()
    return tweet

# Apply the cleaning function to the 'Tweet' column
df['Cleaned Tweet'] = df['Tweet'].apply(clean_tweet)

# Display the modified dataframe
df[['Date', 'Tweet', 'Cleaned Tweet']]

<ipython-input-19-759f2852dc7c>:14: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['Cleaned Tweet'] = df['Tweet'].apply(clean_tweet)


from nltk import pos_tag

# Preprocessing function
def preprocess_text(text):
    # Convert to lowercase
    text = text.lower()
    # Remove punctuation
    text = re.sub(r'[^\w\s]', '', text)
    # Remove digits
    text = re.sub(r'\d+', '', text)
    # Tokenize the text
    tokens = word_tokenize(text)
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [word for word in tokens if word not in stop_words]
    # POS tagging
    pos_tags = pos_tag(filtered_tokens)
    # Lemmatization
    lemmatizer = WordNetLemmatizer()
    lemmatized_text = []
    for word, tag in pos_tags:
        if tag.startswith('NN'):  # Noun
            lemma = lemmatizer.lemmatize(word, pos='n')
        elif tag.startswith('VB'):  # Verb
            lemma = lemmatizer.lemmatize(word, pos='v')
        elif tag.startswith('JJ'):  # Adjective
            lemma = lemmatizer.lemmatize(word, pos='a')
        else:
            lemma = word
        lemmatized_text.append(lemma)
    # Join the lemmatized tokens back into a single string
    preprocessed_text = ' '.join(lemmatized_text)
    return preprocessed_text

# Apply preprocessing to 'Title' and 'Content' columns
df['Cleaned Tweet'] = df['Cleaned Tweet'].apply(preprocess_text)

# Display the preprocessed DataFrame
df

<ipython-input-21-04db43ba880b>:36: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['Cleaned Tweet'] = df['Cleaned Tweet'].apply(preprocess_text)


# Generate word cloud
text = ' '.join(df['Cleaned Tweet'])
generate_word_cloud(text)


# Function to clean tweets
def clean_tweet(tweet):
    # Remove 'tsla' and 'tesla' (case-insensitive)
    tweet = re.sub(r'\b(tsla|tesla)\b', '', tweet, flags=re.IGNORECASE)
    # Remove extra spaces
    tweet = re.sub(r'\s+', ' ', tweet).strip()
    return tweet

# Apply the cleaning function to the 'Tweet' column
df['Cleaned Tweet'] = df['Cleaned Tweet'].apply(clean_tweet)

# Display the modified dataframe
df[['Date', 'Tweet', 'Cleaned Tweet']]

<ipython-input-23-d09bbf052268>:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['Cleaned Tweet'] = df['Cleaned Tweet'].apply(clean_tweet)


# Generate word cloud
text = ' '.join(df['Cleaned Tweet'])
generate_word_cloud(text)


df.to_csv('preprocessed_tweets.csv', index=False)


# prompt: scrape TSLA stock prices from 2021-09-30 to 2024-05-19 from yfinance

import yfinance as yf
import pandas as pd

# Define the ticker symbol and date range
ticker_symbol = "TSLA"
start_date = "2021-09-30"
end_date = "2024-05-19"

# Use yfinance to download historical data
historical_data = yf.download(ticker_symbol, start=start_date, end=end_date)

# Convert the data to a Pandas DataFrame
df = pd.DataFrame(historical_data)
df

# Save the DataFrame to a CSV file
df.to_csv('historical_stock_prices.csv', index=True)

[*********************100%%**********************]  1 of 1 completed

df


df1.shape

(6300, 8)


df.drop(['Open', 'High', 'Low', 'Close'], axis=1, inplace=True)
df.head()


df.shape

(662, 2)


# @title Volume

from matplotlib import pyplot as plt
df['Volume'].plot(kind='line', figsize=(8, 4), title='Volume')
plt.gca().spines[['top', 'right']].set_visible(False)


from matplotlib import pyplot as plt
df['Adj Close'].plot(kind='hist', bins=20, title='Adj Close')
plt.gca().spines[['top', 'right',]].set_visible(False)


df['Adj Close'].plot(kind='line', figsize=(8, 4), title='Adjusted Closing Price')
plt.gca().spines[['top', 'right']].set_visible(False)


df.isnull().sum()

Adj Close    0
Volume       0
dtype: int64


df.to_csv('historical_prices.csv', index=True)


!pip install requests

Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.2.2)


import re
import requests
from bs4 import BeautifulSoup
import json
import pandas as pd
from datetime import datetime

headers = {
    "Cookie": "AJOT2_SessionId=pggqus1ldfo0bwzneawsxjev; American_Occupational_Therapy_AssociationMachineID=638465226214394544; _ga=GA1.3.913198106.1710925824; _gid=GA1.3.1309327130.1710925824; hubspotutk=45c7ea6f7f9bc84af981be5458cbd84f; __hssrc=1; _gcl_au=1.1.585721882.1710925826; _ga=GA1.1.913198106.1710925824; fpestid=4kF3LjnaKNrsHvB8tW4_YKTLTPNsD6vCuAlY0WCMKU5aAwe6XWWv6UtXpHzRbXLsSv41wA; _fbp=fb.1.1710925826667.1584579234; sa-user-id=s%253A0-e2678d88-10ca-5085-6e7f-9699543ec39e.aIOhjYU93e7TrKIeWR6gMMmTxJ02LKzL9HBy03tu1O8; sa-user-id-v2=s%253A4meNiBDKUIVuf5aZVD7DnsD4CYs.VtEorakjnt%252FJQzeCUbY79ctfGiblf2HOIqPboSWhdv8; sa-user-id-v3=s%253AAQAKIDXsHk-nPieIBwxcewSN2DK_yU_oPgQnkfXYHwGkT2yaEAEYAyCIiJCuBjABOgQhyhL0QgRXNvD_.c4W0g0ObZSPQuXL6BfKED0nlq%252BmsAqIVpcRJoitHQOc; _clck=1vryupj%7C2%7Cfk9%7C0%7C1540; AOTATest=ok; visid_incap_531175=Zy2nzuSmR5yFKFg6O/LRzFEC/GUAAAAAQUIPAAAAAAAwq1h6OkWoDAVnjInquuek; incap_ses_1789_531175=z+8RXSSzxndaBkzgDs7TGFIC/GUAAAAAadGLWY1wNHMxfyo/d3eR9w==; __hstc=204606671.45c7ea6f7f9bc84af981be5458cbd84f.1710925825505.1711010287800.1711014485331.3; __hssc=204606671.3.1711014485331; _uetsid=b08c93e0e69911ee8a2b1968b6f72485; _uetvid=b08cc970e69911ee91acfbf279c55fdb; _clsk=f67khb%7C1711015485276%7C7%7C1%7Ck.clarity.ms%2Fcollect; _ga_3CVRFYYS90=GS1.1.1711012705.3.1.1711015489.58.0.0",
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
}
def get_soup(url):
    response = requests.get(url,headers=headers)
    soup= BeautifulSoup(response.content, 'html.parser')
    return soup
def get_content(url):
    content=requests.get(url).content
    soup = BeautifulSoup(content, 'html.parser')
    return soup.text
data=[]
num_pages=400
offset = 0
for page in range(num_pages):
    url = f'https://www.reuters.com/pf/api/v3/content/fetch/articles-by-search-v2?query=%7B%22keyword%22%3A%22tesla%22%2C%22offset%22%3A{offset}%2C%22orderby%22%3A%22display_date%3Adesc%22%2C%22size%22%3A20%2C%22website%22%3A%22reuters%22%7D&d=190&_website=reuters'
    soup = json.loads(get_content(url))
    articles_info = [(article['title'], article['updated_time']) for article in soup['result']['articles']]

    for title, updated_time in articles_info:
        formatted_date = datetime.strptime(updated_time, '%Y-%m-%dT%H:%M:%S.%fZ').strftime('%Y-%m-%d')
        print(f"title='{title}', updated_time='{formatted_date}'")
        data.append({"title": title, "updated_time": formatted_date})

    df = pd.DataFrame(data)
    df.to_csv('tesla_news.csv', index=False)
    print("Data has been successfully saved to tesla_news.csv")
    offset += 20

title='Musk, Indonesian health minister, launch Starlink for health sector', updated_time='2024-05-19'
title='Mercedes workers in Alabama reject union, dealing setback to UAW', updated_time='2024-05-18'
title='UAW's stinging loss in Alabama won't kill national organizing effort', updated_time='2024-05-18'
title='Canada exploring higher tariffs on Chinese EVs, Bloomberg News reports', updated_time='2024-05-17'
title='Exclusive: Shanghai eases data-export curbs sought by Tesla, other firms', updated_time='2024-05-17'
title='Tesla chair sees challenges in getting shareholder vote for Musk's pay package, FT reports', updated_time='2024-05-17'
title='Exclusive: Musk pushes plan for China data to power Tesla's AI ambitions', updated_time='2024-05-17'
title='Tesla told law firm ‘relationship about to be terminated’ in spat over law professor brief – email', updated_time='2024-05-16'
title='Ford asks suppliers to cut costs in push to turn EV business profitable, memo says', updated_time='2024-05-16'
title='Tesla gets local council go-ahead for German factory expansion', updated_time='2024-05-16'
title='Fewer people in the US plan to buy EVs this year, study shows', updated_time='2024-05-16'
title='Japan's Honda raises electrification investment to $65 bln through FY2030', updated_time='2024-05-16'
title='Swedish court rejects Tesla appeal in licence plate case', updated_time='2024-05-16'
title='Toyota tests new EV pickup truck ahead of mass production in Thailand', updated_time='2024-05-16'
title='Musk scheduled to visit Indonesia for Starlink launch, ministers says', updated_time='2024-05-16'
title='China's Nio launches Onvo brand to challenge Tesla's best-selling model', updated_time='2024-05-16'
title=''What doesn't kill you makes you stronger,' China trolls new US tariffs', updated_time='2024-05-16'
title='Hedge fund Coatue scraps its Apple stake in Q1, while Viking built its position', updated_time='2024-05-15'
title='Tesla must face vehicle owners' lawsuit over self-driving claims', updated_time='2024-05-15'
title='The inside story of Elon Musk’s mass firings of Tesla Supercharger staff', updated_time='2024-05-15'
Data has been successfully saved to tesla_news.csv
title='Bridgewater piled into Mag Seven stocks in Q1 but spurned Tesla - filings', updated_time='2024-05-15'
title='SK Innovation considering sale of battery materials unit SKIET, paper reports', updated_time='2024-05-15'
title='US, Canadian companies kick off 2024 with layoffs', updated_time='2024-05-15'
title='Xiaomi becomes 8th largest EV upstart in China after successful SU7 launch', updated_time='2024-05-15'
title='Tesla plans to cut 601 more jobs in California, notice to government says', updated_time='2024-05-15'
title='Nasdaq hits record close after Powell reassures investors, CPI in focus', updated_time='2024-05-14'
title='Elon Musk ordered to testify again in US SEC probe of Twitter takeover', updated_time='2024-05-14'
title='A US-China EV trade war threatens Biden's clean-car agenda', updated_time='2024-05-14'
title='Tesla labour dispute triggers Nordic sympathy strikes', updated_time='2024-05-14'
title='Tesla strike in Sweden heats up as nation's largest union joins fray', updated_time='2024-05-14'
title='Tesla is sued over emissions from California plant', updated_time='2024-05-14'
title='Chevron tops Tesla as most-shorted stock in April, says Hazeltree', updated_time='2024-05-14'
title='Tesla threatened to fire law firm in bid to block Musk pay critic – court document', updated_time='2024-05-13'
title='Tesla settles factory worker's sexual harassment lawsuit', updated_time='2024-05-13'
title='Tim Draper leads $3.5 mln funding round for six-employee startup behind bitcoin lending', updated_time='2024-05-13'
title='Chinese EV maker Zeekr soars nearly 35% in stellar US market debut', updated_time='2024-05-10'
title='Protesters attempt to storm Tesla German plant, clash with police', updated_time='2024-05-10'
title='Tesla's EV charging team layoffs threaten to slow Biden's program to electrify highways', updated_time='2024-05-10'
title='Musk's X Corp loses lawsuit against Israeli data-scraping company', updated_time='2024-05-10'
title='After layoffs, Musk says Tesla to spend $500 million on charging network', updated_time='2024-05-11'
Data has been successfully saved to tesla_news.csv
title='Elon Musk clearly sees AI hallucinations', updated_time='2024-05-10'
title='Chinese EV makers set sights on European production', updated_time='2024-05-10'
title='China's car exports hit record high in April, as domestic sales fall', updated_time='2024-05-10'
title='BP looks to expand EV charging unit in US after Tesla disbands Supercharger team', updated_time='2024-05-09'
title='Elon Musk may be compelled to testify again in SEC's Twitter takeover probe', updated_time='2024-05-09'
title='Tesla interfered with union organizing at New York plant, US agency claims', updated_time='2024-05-09'
title='Brembo tempers revenue forecast despite core profit rise', updated_time='2024-05-09'
title='How homegrown startups are boosting e-mobility in Africa', updated_time='2024-05-13'
title='Panasonic's battery unit misses annual profit estimate', updated_time='2024-05-09'
title='Dow ends higher for 6th session, but Treasury yields pressure market', updated_time='2024-05-08'
title='Exclusive: In Tesla Autopilot probe, US prosecutors focus on securities, wire fraud', updated_time='2024-05-08'
title='Piedmont Lithium wins US state mining permit after posting reclamation bond', updated_time='2024-05-08'
title='Hyundai Motor plans to add hybrids to US plant within current investment -exec', updated_time='2024-05-08'
title='BMW says costs, low used car prices to weigh on 2024 pre-tax profit', updated_time='2024-05-08'
title='Morning Bid: Stocks buoyant as Sweden cuts, oil skids', updated_time='2024-05-08'
title='Elon Musk proposed to launch robotaxis in China during April visit, state media report says', updated_time='2024-05-08'
title='Luminar says Tesla is biggest customer for its lidar sensors', updated_time='2024-05-07'
title='S&P, Dow end slightly up, extend closing streaks despite Disney drag', updated_time='2024-05-07'
title='Tesla's China-made EV sales fall 18% in April; shares slip', updated_time='2024-05-07'
title='Tesla confirms 'one day' shutdown at German factory on May 10', updated_time='2024-05-07'
Data has been successfully saved to tesla_news.csv
title='US seeks answers from Tesla in Autopilot recall probe', updated_time='2024-05-07'
title='Toyota to get an earnings lift from hybrids as EV hype cools', updated_time='2024-05-07'
title='Exclusive: China's Nio turns to rival BYD to power new EV brand', updated_time='2024-05-07'
title='Reuters wins two Pulitzers, ProPublica takes coveted public service award', updated_time='2024-05-06'
title='Tesla lays off more staff in software, service teams, Electrek reports', updated_time='2024-05-06'
title='Nio on track to unveil its first mass-market car in May, executive says', updated_time='2024-05-06'
title='Tesla vs Tesla: US carmaker sues Indian namesake for copying trademark', updated_time='2024-05-03'
title='Fact Check: Musk quote on imprisoning Gates stems from satire', updated_time='2024-05-03'
title='US ends Tesla rear-view camera investigation after 2021 recall', updated_time='2024-05-03'
title='Tech giants' market cap falls on AI doubts, high rates; Alphabet, Tesla gain', updated_time='2024-05-02'
title='Exclusive: Tesla retreats from next-generation ‘gigacasting’ manufacturing process', updated_time='2024-05-02'
title='Chipmaker Wolfspeed forecasts quarterly revenue below estimates as EV sales growth slows', updated_time='2024-05-01'
title='Elon Musk's Tesla overhaul hits executive bench he touted', updated_time='2024-05-01'
title='Musk’s China jaunt keeps underwhelming status quo', updated_time='2024-05-02'
title='Crypto washout sends bitcoin below $58,000 ahead of Fed decision', updated_time='2024-05-01'
title='Musk disbands Tesla EV charging team, leaving customers in the dark', updated_time='2024-05-01'
title='Amazon results beat estimates, revenue forecast misses', updated_time='2024-05-01'
title='Australia's Anson Resources inks lithium carbonate supply deal with LG Energy', updated_time='2024-05-01'
title='Wall Street stocks fall as markets weigh strong wage data, Fed meeting', updated_time='2024-04-30'
title='Weight-loss drug drives Eli Lilly to raise 2024 sales forecast by $2 billion', updated_time='2024-04-30'
Data has been successfully saved to tesla_news.csv
title='India 'shocked' as Musk's surprise China visit leaves them spurned', updated_time='2024-05-01'
title='Amazon.com may be feeling pressure to join the dividend club', updated_time='2024-04-30'
title='Weak demand, new model ramp up hit Europe's top automakers', updated_time='2024-04-30'
title='Musk lays off Tesla senior executives in fresh job cuts, The Information reports', updated_time='2024-04-30'
title='Canada risks losing investment to Mexico as labor productivity skids', updated_time='2024-04-30'
title='Morning Bid: Yen slips anew with Amazon due, Treasury plans irk', updated_time='2024-04-30'
title='Tesla's self-driving bid for China faces rivals racing ahead', updated_time='2024-04-30'
title='Wall Street stocks finish higher as investors focus on Fed moves', updated_time='2024-04-29'
title='Morning Bid: Dollar recoils at 160 yen, Tesla's China joy', updated_time='2024-04-29'
title='EEOC says workplace bias laws cover bathrooms, pronouns, abortion', updated_time='2024-04-29'
title='Tesla's California registrations down second quarter in a row, dealer data shows', updated_time='2024-04-29'
title='US Supreme Court won't hear Elon Musk dispute over SEC settlement', updated_time='2024-04-29'
title='In China, Elon Musk scores wins on the path to self-driving cars', updated_time='2024-04-29'
title='Mercedes unionization vote in Alabama will likely get a boost from UAW deal with Daimler Truck ', updated_time='2024-04-29'
title='Tesla short sellers lose nearly $5.5 bln over four days, S3 Partner says', updated_time='2024-04-29'
title='US opens probe into 130,000 Ford vehicles over hands-free tech', updated_time='2024-04-29'
title='Second global AI safety summit faces tough questions, lower turnout', updated_time='2024-04-29'
title='China's BYD shows effects of price war with weaker first-quarter earnings', updated_time='2024-04-29'
title='Morning Bid: Yen bounces from 160 per dollar in busy Fed-led week', updated_time='2024-04-29'
title='What is Tesla's Full Self-Driving and why its China rollout matters', updated_time='2024-04-29'
Data has been successfully saved to tesla_news.csv
title='Baidu, Tesla agree on mapping deal for FSD in China, sources say', updated_time='2024-04-29'
title='Morning Bid: The yen's mysterious move', updated_time='2024-04-29'
title='Tesla clears key China assisted-driving hurdle with Baidu deal, Bloomberg News says', updated_time='2024-04-29'
title='Morning Bid: Helter-skelter yen, US tech tonic', updated_time='2024-04-28'
title='Elon Musk visits China as Tesla seeks self-driving technology rollout', updated_time='2024-04-28'
title='Take Five: Fed straight ahead', updated_time='2024-04-27'
title='Wall Street shares close up as megacap tech stocks rally', updated_time='2024-04-27'
title='Wall St Week Ahead Lofty US stocks leave investors punishing earnings disappointments', updated_time='2024-04-27'
title='Tesla to lay off 693 employees in Nevada, government notice says', updated_time='2024-04-26'
title='Two senators want US to order Tesla to restrict Autopilot use', updated_time='2024-04-26'
title='US probes Tesla recall of 2 million vehicles over Autopilot', updated_time='2024-04-26'
title='Insight: Why BYD's EV exports sell for twice the China price', updated_time='2024-04-26'
title='Tesla's plan for affordable cars takes page from Detroit rivals', updated_time='2024-04-26'
title='Former exec Baglino sells Tesla shares worth $181.5 mln, SEC filing shows', updated_time='2024-04-26'
title='Bosch to launch urban navigate-on-autopilot functions in Chinese cities', updated_time='2024-04-26'
title='Battle for 'tech luxury' in China as EVs get smarter - and cheaper', updated_time='2024-04-25'
title='China's largest auto show showcases all-electric future, local brands dominate', updated_time='2024-04-25'
title='Morning bid: Meta plunges, mega mining merger revealed', updated_time='2024-04-25'
title='Hyundai Motor Q1 hurt by weak home sales, doubles down on hybrids, India', updated_time='2024-04-25'
title='STMicro cuts FY revenue outlook as softer car demand weighs', updated_time='2024-04-25'
Data has been successfully saved to tesla_news.csv
title='China's BYD seeks to redefine luxury for the EV generation', updated_time='2024-04-25'
title='LG Energy Solution to minimise capex this year due to slow EV demand', updated_time='2024-04-25'
title='S&P 500 ends higher as markets weigh rising yields, upbeat corporate results', updated_time='2024-04-24'
title='Ford profit beats on commercial sales; EVs still dragging', updated_time='2024-04-24'
title='Tesla shareholder seeks to bar Musk from dodging Delaware pay ruling', updated_time='2024-04-25'
title='How Volvo landed a cheap Chinese EV on U.S. shores in a trade war', updated_time='2024-04-24'
title='Tesla jumps as Musk's promise of 'more affordable' cars eases growth fears', updated_time='2024-04-24'
title='Volkswagen aims to keep China market share stable as price war rages', updated_time='2024-04-24'
title='Beijing powers up into car wars battleground', updated_time='2024-04-25'
title='Tesla expects to book over $350 million in costs for layoffs', updated_time='2024-04-24'
title='Morning Bid: Tesla catches break, Meta next; TikTok countdown', updated_time='2024-04-24'
title='Morning Bid: Yen on the brink, but Tesla pulls back', updated_time='2024-04-24'
title='China's EV survival game creates peril - and power', updated_time='2024-04-24'
title='Tesla promises 'more affordable' cars after shelving all-new Model 2', updated_time='2024-04-24'
title='Tesla's shift on low-cost cars throws Mexico, India factory plans into limbo', updated_time='2024-04-24'
title='Tesla offers zero down payments on Model 3, Model Y in China', updated_time='2024-04-24'
title='LG Energy Solution to license out battery patents to block tech infringement, exec says', updated_time='2024-04-24'
title='Tesla prices Model 3 performance version at $46,364 in China, to deliver from Q3', updated_time='2024-04-24'
title='Tesla could start selling Optimus robots by the end of next year, Musk says', updated_time='2024-04-24'
title='Wall Street closes higher as investors digest earnings, megacap outlook', updated_time='2024-04-24'
Data has been successfully saved to tesla_news.csv
title='Musk's X Corp appeals dismissal of lawsuit against anti-hate group', updated_time='2024-04-23'
title='Stocks gear up for Big Tech earnings; yen touches multi-year lows', updated_time='2024-04-23'
title='UAW moving quickly after VW organizing win, union president says', updated_time='2024-04-23'
title='General Motors results beat targets, forecast raised, shares jump', updated_time='2024-04-23'
title='Tesla to cut more than 6,000 jobs in Texas, California, notices show', updated_time='2024-04-23'
title='Tesla to lay off more than 3,000 employees in California, notices show', updated_time='2024-04-23'
title='Tesla shares rise ahead of quarterly results', updated_time='2024-04-23'
title='Tesla launches performance variant of Model 3 compact sedan', updated_time='2024-04-23'
title='Tesla targeting about 400 voluntary job cuts in Germany', updated_time='2024-04-23'
title='Renault says new models to lift 2024 sales, confirms profit outlook', updated_time='2024-04-23'
title='Reuters World News: Ukraine's depleted army, and other top stories', updated_time='2024-04-24'
title='Morning Bid: Tanking Tesla reports, Europe business beats', updated_time='2024-04-23'
title='Morning Bid: Eyes on PMIs for rebound signs', updated_time='2024-04-23'
title='Wall St stocks end higher with major corporate earnings in view', updated_time='2024-04-22'
title=''Overdue' pullback in US stocks to test dip-buyers' resolve', updated_time='2024-04-22'
title='Morning Bid: Tech becalmed, April PMIs start rolling in', updated_time='2024-04-22'
title='Tesla expected to post lower profit margin amid doubts on growth, product strategy', updated_time='2024-04-22'
title='Short sellers pocket record weekly profit from Big Tech selloff', updated_time='2024-04-22'
title='EU's 2050 net zero goals at risk as EV rollout faces setbacks', updated_time='2024-04-22'
title='China's state planner warns intensified EV price war on oversupply', updated_time='2024-04-22'
Data has been successfully saved to tesla_news.csv
title='Tesla cuts prices globally to spur demand', updated_time='2024-04-22'
title='Tesla shares tumble on price cuts in run-up to earnings', updated_time='2024-04-22'
title='Floods swamp southern China, spark extreme weather fears', updated_time='2024-04-22'
title='After VW plant victory, UAW sets its sights on Mercedes in Alabama', updated_time='2024-04-22'
title='Morning Bid: Big Tech swoons as Q1s lurk, Nvidia decimated', updated_time='2024-04-22'
title='UAW wins big in historic union vote at Volkswagen Tennessee factory', updated_time='2024-04-22'
title='Automakers hope for a cut as two-way EV charging becomes real', updated_time='2024-04-22'
title='Take Five: Dancing to the dollar's beat', updated_time='2024-04-22'
title='Tesla cuts prices in China, Germany and around globe after US cuts', updated_time='2024-04-22'
title='China's Li Auto cuts car prices in market share battle', updated_time='2024-04-22'
title='Brazil's Bolsonaro rallies supporters in Rio', updated_time='2024-04-21'
title='Tesla cuts prices of some models in Germany and Europe', updated_time='2024-04-21'
title='Tesla cuts price of Full Self-Driving software by a third to $8,000', updated_time='2024-04-21'
title='Wall St Week Ahead 'Crowded' megacap trade in US stocks awaits earnings test', updated_time='2024-04-21'
title='Tesla's Elon Musk postpones India trip, aims to visit this year', updated_time='2024-04-20'
title='Musk says Tesla's Full Self-Driving may enter China very soon', updated_time='2024-04-20'
title='Tesla cuts US prices of Models Y, X, S by $2,000', updated_time='2024-04-20'
title='UAW ahead as first ballots counted in union vote at Volkswagen's Tennessee factory', updated_time='2024-04-20'
title='Tesla Semi trucks in short supply for PepsiCo as its rivals use competing EV big rigs', updated_time='2024-04-19'
title='Tesla to recall 3,878 Cybertrucks over faulty accelerator pedal, NHTSA says', updated_time='2024-04-19'
Data has been successfully saved to tesla_news.csv
title='Exclusive: India's Ola Cabs plans $500 million IPO, to appoint banks soon, sources say', updated_time='2024-04-19'
title='GM and Ford count on gas-powered trucks as EV growth slows', updated_time='2024-04-19'
title='Morning Bid: Markets unsettled by Israel-Iran tit-for-tat', updated_time='2024-04-19'
title='T. Rowe Price says 2018 Tesla pay aligned with investor interests', updated_time='2024-04-18'
title='Rivian cuts 1% of workforce in second round of layoffs this year', updated_time='2024-04-18'
title='Tesla tries legal 'Band-Aid' to revive Musk's huge pay deal', updated_time='2024-04-18'
title='Tesla shares fall as Deutsche Bank flags risks from focus on Robotaxi', updated_time='2024-04-18'
title='Tata plans Jaguar Land Rover EV imports to India under new policy, sources say', updated_time='2024-04-18'
title='Musk’s next big payday matters more than his last', updated_time='2024-04-18'
title='Exclusive: Mexico, facing US pressure, will halt incentives to Chinese EV makers', updated_time='2024-04-18'
title='Tesla says 300 subcontracted workers let go in Germany', updated_time='2024-04-18'
title='Early Easter causes EU new car sales to drop 5.2% in March, says ACEA', updated_time='2024-04-18'
title='Wobbling US stocks could push volatility-linked funds to ramp up selling', updated_time='2024-04-18'
title='Tesla layoffs include 14% of Buffalo workers, WARN notice shows', updated_time='2024-04-17'
title='Tesla again seeks shareholder approval for Musk's 2018 pay voided by judge', updated_time='2024-04-17'
title='Wall Street wants answers from Elon Musk on Tesla's Model 2 car', updated_time='2024-04-17'
title='Tesla to lay off 300 temporary workers at German site, Business Insider says', updated_time='2024-04-17'
title=' UAW Volkswagen Tennessee plant vote: Union seeks landmark win', updated_time='2024-04-17'
title='Tesla's Musk likely to unveil $2-$3 bln India investment during visit, sources say', updated_time='2024-04-17'
title='Chinese automaker FAW's auto finance unit launches loan with no down payment', updated_time='2024-04-17'
Data has been successfully saved to tesla_news.csv
title='Volkswagen to roll out new architecture with Xpeng to cut China EV costs', updated_time='2024-04-17'
title='Japan's Nissan bets on solid-state batteries, gigacasting for next-gen EVs', updated_time='2024-04-17'
title='Wall Street stocks close lower on higher Treasury yields, rate expectations', updated_time='2024-04-16'
title='Tesla's global job cuts include leading markets US, China', updated_time='2024-04-17'
title='Tesla's global job cuts hit China sales team, sources say', updated_time='2024-04-16'
title='UK's Octopus to lease out China's Omoda EVs, offer charging services', updated_time='2024-04-15'
title='Tesla laying off more than 10% of staff globally as sales fall', updated_time='2024-04-16'
title='Stocks close lower as Middle East tensions, Treasury yields weigh', updated_time='2024-04-15'
title='Tesla supplier Piedmont Lithium gets key North Carolina mining permit', updated_time='2024-04-15'
title='Exclusive: BP's EV charging arm cuts jobs, reduces global ambitions', updated_time='2024-04-15'
title='Tesla scouts for its first India showroom locations, sources say', updated_time='2024-04-15'
title='China EV maker Nio urges openness in US speech amid tensions', updated_time='2024-04-14'
title='US issues auto dealers over $580 million in advance EV tax rebates this year', updated_time='2024-04-12'
title='Tesla cuts Full Self-Driving subscription prices in US, Canada', updated_time='2024-04-12'
title='Musk, Argentine president see eye-to-eye on boosting free markets and lithium', updated_time='2024-04-12'
title='Chinese body says EU electric vehicle probe is stacked against China', updated_time='2024-04-12'
title='Norway's KLP may push Tesla on collective bargaining at annual meeting', updated_time='2024-04-12'
title='Hong Kong catches tantalising glimpse of future', updated_time='2024-04-12'
title='US says Ford was using driver assistance during fatal Texas crash', updated_time='2024-04-11'
title='Ford cuts prices for F-150 Lightning XLT, Flash and Lariat variants', updated_time='2024-04-11'
Data has been successfully saved to tesla_news.csv
title='Soaring insurance costs hit as US buyers get a break on car prices', updated_time='2024-04-11'
title='Chinese car manufacturing in Italy could force tough decisions, says Stellantis CEO', updated_time='2024-04-10'
title='Musk to meet Modi in India; sources say announcement on investment likely', updated_time='2024-04-10'
title='US NTSB chair vows to be 'fierce advocate' for safety in new term', updated_time='2024-04-10'
title='Tesla strike in Sweden continues, union says, contradicting Musk', updated_time='2024-04-10'
title='Norwegian fund’s podcast guru plays both sides', updated_time='2024-04-10'
title='China has been closing idled auto output capacity, industry body says', updated_time='2024-04-10'
title='China's EV export boom fuels surge in demand for new car-carrying ships', updated_time='2024-04-10'
title='Musk lawyer Alex Spiro faces sanctions bid in Texas defamation fight', updated_time='2024-04-09'
title='EV maker Lucid beats first-quarter delivery estimates as price cuts boost demand', updated_time='2024-04-09'
title='Argentina's Milei to visit Tesla factory, meet Musk this week', updated_time='2024-04-09'
title='China's first-quarter EV sales growth slowest in a year', updated_time='2024-04-09'
title='Symbolica raises $31 mln to develop AI systems to compete with OpenAI', updated_time='2024-04-09'
title='Former Tesla CEO says it is a 'shame' company is cancelling low-cost car plans', updated_time='2024-04-09'
title='Tesla settles case over fatal Autopilot crash of Apple engineer', updated_time='2024-04-09'
title='Wall St ends flat as investors await CPI, earnings', updated_time='2024-04-08'
title='Tesla's Musk predicts AI will be smarter than the smartest human next year', updated_time='2024-04-08'
title='Wells Fargo boosts end-2024 target on S&P 500 to Street-high of 5,535', updated_time='2024-04-08'
title='China's Didi, GAC Aion to mass produce robotaxis next year', updated_time='2024-04-08'
title='Hyundai Motor, Kia to partner with India's Exide Energy on EV batteries', updated_time='2024-04-08'
Data has been successfully saved to tesla_news.csv
title='SAIC's MG brand to bring in JSW and other local investors in India', updated_time='2024-04-08'
title='Tesla's bet on robotaxis is a long way from paying off', updated_time='2024-04-06'
title='Tesla casts doubt on the quest for a Model T of EVs', updated_time='2024-04-06'
title='Alabama Mercedes Benz plant workers file for union election, UAW says', updated_time='2024-04-05'
title='Wall Street indexes rise after strong jobs data', updated_time='2024-04-05'
title='Musk says Tesla will unveil 'Robotaxi' on August 8', updated_time='2024-04-05'
title='Exclusive: Tesla scraps low-cost car plans amid fierce Chinese EV competition', updated_time='2024-04-06'
title='Wall St Week Ahead US stocks' lofty valuations in spotlight as earnings season nears', updated_time='2024-04-05'
title='Tesla accused in lawsuit of cheating California workers out of wages', updated_time='2024-04-05'
title='Elon Musk bets big on Tesla, not cars', updated_time='2024-04-05'
title='VIEW Tesla scraps low-cost car plans amid fierce Chinese EV competition', updated_time='2024-04-05'
title='Tesla's bumpy ride, captured in five charts', updated_time='2024-04-05'
title='India EV sales to rise 66% this year after nearly doubling in 2023, report says', updated_time='2024-04-05'
title='Hedge funds rally in Q1, powered by gains in equities', updated_time='2024-04-04'
title='Active US funds tally best quarterly outperformance since 2007, BofA says', updated_time='2024-04-04'
title='Nvidia, Alphabet lead market cap surge in March', updated_time='2024-04-04'
title='Tesla begins making cars in Germany for export to India this year -sources', updated_time='2024-04-04'
title='Volvo Cars posts record March sales, lifted by small electric SUV', updated_time='2024-04-04'
title='US Senate to consider nomination of NTSB chair for new term', updated_time='2024-04-03'
title='Tesla to scout sites in India for $2 bln-$3 bln EV factory, FT reports', updated_time='2024-04-03'
Data has been successfully saved to tesla_news.csv
title='With wary eye, China courts Indonesia's incoming leader Prabowo', updated_time='2024-04-03'
title='Chinese smartphone maker Xiaomi starts delivering the first of more than 100,000 EVs ordered', updated_time='2024-04-03'
title='Mercedes workers in Alabama to file for union vote this week, UAW leader says', updated_time='2024-04-03'
title='Stocks fall, yields climb as rate cut outlook takes a hit', updated_time='2024-04-03'
title='Indexes end lower as Tesla drops, rate cut timing weighed', updated_time='2024-04-02'
title='Morning Bid: Investors hunker down as perfect storm breaks', updated_time='2024-04-02'
title='Tesla quarterly deliveries decline for the first time in nearly four years', updated_time='2024-04-02'
title='GM's first-quarter US sales dip on lower commercial deliveries but beats Toyota', updated_time='2024-04-02'
title='Rivian deliveries beat expectations; Tesla dampens industry hopes', updated_time='2024-04-02'
title='Tesla exacerbates valuation range anxiety', updated_time='2024-04-03'
title='BYD hands back top EV seller title to Tesla after Q1 sales decline', updated_time='2024-04-02'
title='EVs could overtake petrol cars in Norway by end-2024', updated_time='2024-04-02'
title='Profitability is next sacrifice at inflation altar', updated_time='2024-04-02'
title='Tesla's China-made EV sales edge up 0.2% y/y in March', updated_time='2024-04-02'
title='EV startup Canoo's 2024 revenue forecast disappoints, shares tumble', updated_time='2024-04-01'
title='Would-be Tesla buyers snub company as Musk's reputation dips', updated_time='2024-04-01'
title='Tesla raises prices of Model Y cars in US by $1,000', updated_time='2024-04-01'
title='Exclusive: China's SAIC aims to slash jobs at GM, VW ventures and EV unit, sources say', updated_time='2024-03-31'
title='US finalizes new tailpipe emissions limits for heavy duty vehicles', updated_time='2024-03-29'
title='With China EV launch, Xiaomi's 'Thor' takes on Elon Musk', updated_time='2024-03-29'
Data has been successfully saved to tesla_news.csv
title='Mazda and Panasonic's energy unit sign auto battery supply agreement', updated_time='2024-03-29'
title='Xiaomi takes aim at Tesla in Chinese auto market with $29,870 electric car', updated_time='2024-03-29'
title='How the US stock market rocketed through the first quarter', updated_time='2024-03-28'
title='Tesla appears unlikely to nix US suit alleging bias against Black workers', updated_time='2024-03-28'
title='Musk questioned in defamation lawsuit over X post about street brawl', updated_time='2024-03-28'
title='Fact Check: Angela Chao was CEO of Foremost Group, not Dali-owner Grace Ocean', updated_time='2024-03-28'
title='Tesla deliveries face hit from China slowdown, soft demand', updated_time='2024-03-28'
title='Exclusive: China commerce minister to head to Europe to make EV case', updated_time='2024-03-28'
title='How companies are responding to attacks on ships', updated_time='2024-03-27'
title='Nissan will invest in Renault's Ampere in pursuit of global EV roadmap, CEO says', updated_time='2024-03-27'
title='Chinese EV maker BYD targets 20% jump in annual sales - sources', updated_time='2024-03-27'
title='Chinese EV maker Nio cuts Q1 deliveries forecast amid tepid demand', updated_time='2024-03-27'
title='Fundamentals vie with 'FOMO' as US tech boom driver', updated_time='2024-03-27'
title='EV maker VinFast signs partnership agreements with 15 dealers in Thailand', updated_time='2024-03-27'
title='Dow, S&P fall for third straight session with inflation data eyed', updated_time='2024-03-26'
title='GM, Ford are upbeat on 'really strong' US demand', updated_time='2024-03-26'
title='Tesla shares gain as Musk offers US customers Full Self-Driving software trial', updated_time='2024-03-26'
title='China EV giant BYD posts slowest quarterly profit growth in 2 years', updated_time='2024-03-26'
title='Renault in advanced talks with partners to recycle batteries, says executive', updated_time='2024-03-26'
title='Tesla offers U.S. customers a month's trial of its driver-assist technology', updated_time='2024-03-26'
Data has been successfully saved to tesla_news.csv
title='Fisker deal talks with big automaker collapse, NYSE to delist stock', updated_time='2024-03-26'
title='Australia softens proposed vehicle emission laws for some pick-up trucks', updated_time='2024-03-26'
title='Musk's X Corp loses lawsuit against hate speech watchdog', updated_time='2024-03-25'
title='Luxury EV maker Lucid to raise $1 bln from Saudi's PIF affiliate', updated_time='2024-03-25'
title='VW workers in Tennessee will vote in April on whether to join UAW', updated_time='2024-03-25'
title='CATL in talks with Tesla, global automakers for US licensing, WSJ reports', updated_time='2024-03-25'
title='Companies invest in EV battery factories in Europe', updated_time='2024-03-25'
title='Nissan to launch 30 new models by 2027, boost global sales volumes', updated_time='2024-03-25'
title='Xiaomi CEO teases price on upcoming car, showrooms begin displaying vehicle', updated_time='2024-03-25'
title='Wall St Week Ahead Broadening US market rally gets boost from dovish Fed', updated_time='2024-03-24'
title='Biden administration urges US Supreme Court to reject Musk appeal in SEC dispute', updated_time='2024-03-22'
title='Tesla trims output of cars in China amid slower EV sales growth, Bloomberg reports', updated_time='2024-03-22'
title='Senator Warren urges SEC to investigate Tesla over board independence', updated_time='2024-03-21'
title='EU new car sales rise 10.1% y/y in February, ACEA says', updated_time='2024-03-21'
title='Wall St ends higher as Fed keeps three rate cuts on the horizon', updated_time='2024-03-20'
title='Tesla says most German plant employees vote against IG Metall union', updated_time='2024-03-20'
title='US eases tailpipe rules, slows EV transition through 2030', updated_time='2024-03-20'
title='Synopsys says new tools help design cars, data centers faster', updated_time='2024-03-20'
title='JSW says MG Motor JV aims to sell 1 mln EVs in India by 2030', updated_time='2024-03-20'
title='Mexican wave of nearshoring firms is all at sea', updated_time='2024-03-20'
Data has been successfully saved to tesla_news.csv
title='Danish central bank raises 2024 GDP forecast on Novo Nordisk, gas field restart', updated_time='2024-03-20'
title='Tesla to raise price of China-produced Model Y vehicles from April 1', updated_time='2024-03-20'
title='Nvidia stock climbs as CFO says new chip to ship in 2024', updated_time='2024-03-20'
title='China-based Canadian stole Tesla secrets, say US prosecutors', updated_time='2024-03-19'
title='White House softens EV mileage rule in a win for automakers', updated_time='2024-03-19'
title='US DOJ to target pre-IPO artificial intelligence frauds, top attorney says', updated_time='2024-03-19'
title='Morning Bid: Japan returns from upside-down world, AI fizzes', updated_time='2024-03-19'
title='Bullish investors pile into EM, European stocks in March, BofA survey shows', updated_time='2024-03-19'
title='UAW says workers at VW Tennessee plant file for union election', updated_time='2024-03-19'
title='US to finalize revised EV mileage rating rule in win for automakers, sources say', updated_time='2024-03-18'
title='Wall Street ends higher, investors juggle Fed nerves with AI enthusiasm', updated_time='2024-03-18'
title='Nvidia expands ties with Chinese EV makers as auto AI race heats up', updated_time='2024-03-18'
title='US agency probes driver assistance system use in fatal Ford crash', updated_time='2024-03-18'
title='Nasdaq resolves technical glitch that impacted premarket trading', updated_time='2024-03-18'
title='Tesla, autopilot crash victim's estate clash over damages in Florida appeal', updated_time='2024-03-18'
title='Tesla shares gain after Model Y price hike in US, Europe', updated_time='2024-03-18'
title='Musk defends his ketamine use as beneficial for investors in new video', updated_time='2024-03-18'
title='China's military, state media slam U.S. after Reuters report on SpaceX spy satellites', updated_time='2024-03-18'
title='CATL earnings slip masks charged-up ambitions', updated_time='2024-03-18'
title='Tesla to raise prices on Model Y electric vehicles in parts of Europe', updated_time='2024-03-16'
Data has been successfully saved to tesla_news.csv
title='Exclusive: Musk's SpaceX is building spy satellite network for US intelligence agency, sources say', updated_time='2024-03-16'
title='Tesla Germany staff to elect new council to gain control over work conditions', updated_time='2024-03-16'
title='Tesla to raise US prices for Model Y vehicles by $1,000 on April 1', updated_time='2024-03-16'
title='Joe Biden out-MAGAs Donald Trump', updated_time='2024-03-16'
title='Tesla settles race bias claims by Black former worker after $3 million verdict', updated_time='2024-03-15'
title='Electric vehicle plans of Indian automakers', updated_time='2024-03-15'
title='In big win for Tesla, India to lower EV import tax if $500 mln invested', updated_time='2024-03-15'
title='Rivals Nissan and Honda sign MoU on EV partnership', updated_time='2024-03-15'
title='Morning Bid: Treasuries torpedoed, Tesla tanked', updated_time='2024-03-15'
title='Nissan considering partnership with Honda on EVs - sources', updated_time='2024-03-15'
title='Macron awards highest French honour to LVMH CEO at star-studded event -reports', updated_time='2024-03-14'
title='Sales of solar batteries for German homes seen rising 26% in 2024', updated_time='2024-03-14'
title='China's plan to cut downpayments for cars likely to fall flat, analysts say', updated_time='2024-03-14'
title='Ford has big goals for software sales to small business truck fleets', updated_time='2024-03-14'
title='Morning Bid: Oil smudges inflation view, Tesla in slow lane', updated_time='2024-03-14'
title='Lithium miner Pilbara Minerals accepts pre-auction offer for spodumene cargo', updated_time='2024-03-14'
title='Musk says 'Deutschland rocks!' after visit to arson-hit Tesla plant', updated_time='2024-03-13'
title='Tesla slips as Wells Fargo warns of waning effect of price cuts over demand', updated_time='2024-03-13'
title='Elon Musk says Tesla factory back online after arson attack', updated_time='2024-03-13'
title='Exclusive: Italy in talks with China's Chery on car plant', updated_time='2024-03-13'
Data has been successfully saved to tesla_news.csv
title='China's Xiaomi embarks on Apple's road not taken', updated_time='2024-03-13'
title='Toyota becomes Asia's most shorted large-cap stock - Hazeltree', updated_time='2024-03-12'
title='Tesla official talks up Southeast Asia expansion as China's BYD pulls ahead', updated_time='2024-03-12'
title='Tesla, rivals get low marks for automated-driving technology', updated_time='2024-03-12'
title='Musk to visit German gigafactory on Wednesday, Table Media reports', updated_time='2024-03-12'
title='Morning Bid: Inflation thermometer tops market dashboard', updated_time='2024-03-12'
title='Tesla arson case shows need to protect power grid, says Germany's Westenergie', updated_time='2024-03-12'
title='Tesla says it will take some time for production at German factory to fully resume', updated_time='2024-03-12'
title='Tesla's German gigafactory reconnected to grid after week-long outage', updated_time='2024-03-11'
title='Elon Musk takes another swing at OpenAI, makes xAI's Grok chatbot open-source', updated_time='2024-03-11'
title='Next Autopilot trial to test Tesla's blame-the-driver defense', updated_time='2024-03-11'
title='Volkswagen quits Australia auto lobby committee as fight grows over green fuel standards', updated_time='2024-03-11'
title='Tesla's German plant to restart next week, says works council head', updated_time='2024-03-09'
title='Rivian gains as new launch R2 SUV attracts strong early orders', updated_time='2024-03-08'
title='Novo Nordisk confident of amycretin obesity drug launch this decade', updated_time='2024-03-08'
title='Nvidia on cusp of overtaking Apple as second-most-valuable company', updated_time='2024-03-08'
title='Germany's federal prosecutor investigates Tesla arson attack', updated_time='2024-03-08'
title='What are EV startups doing to ride out weak demand?', updated_time='2024-03-08'
title='Nickel rout is energy-transition warning for West', updated_time='2024-03-08'
title='China EV sales slow in January-February; price-cutting intensifies', updated_time='2024-03-08'
Data has been successfully saved to tesla_news.csv
title='Reuters World News: TikTok crackdown, and other top stories', updated_time='2024-03-11'
title='Morning Bid: Surf's back up on Wall Street', updated_time='2024-03-08'
title='Nissan may bring ultra-compact EV production in-house from 2028, sources say', updated_time='2024-03-08'
title='BYD spearheads Chinese electric car push in Australia, a friendlier market', updated_time='2024-03-08'
title='Australia auto lobby split widens as Tesla, Volkswagen back tougher emissions plan', updated_time='2024-03-08'
title='Tesla drivers lose US class action bid in battery range cases', updated_time='2024-03-08'
title='EV maker Rivian faces turning point with debut of cheaper R2', updated_time='2024-03-07'
title='Novo valuation surpasses Tesla on experimental obesity drug data', updated_time='2024-03-07'
title='EVs will be cheaper to produce than gas-powered vehicles by 2027, Gartner says', updated_time='2024-03-07'
title='Tesla says German plant power outage to continue until end of next week', updated_time='2024-03-07'
title='Tesla quits Australia auto lobby over claims on car emission standards', updated_time='2024-03-07'
title='Wall Street indexes advance as Fed's Powell fuels hopes for rate cuts this year', updated_time='2024-03-06'
title='Musk says X could soon receive payment licenses in New York, California', updated_time='2024-03-06'
title='Trump got the voters on Super Tuesday, but his cash-hungry campaign needs more donors', updated_time='2024-03-06'
title='Tesla shares fall after Morgan Stanley cuts price target on weakening EV demand', updated_time='2024-03-06'
title='After Trump meeting, Musk says he won't donate to either candidate', updated_time='2024-03-07'
title='China's ability to flood EV market concerning to US, energy secretary says', updated_time='2024-03-06'
title='OpenAI hits back at Musk, says billionaire pushed for merger with Tesla', updated_time='2024-03-06'
title='Pirelli to focus on new materials, connectivity for future M&A strategy', updated_time='2024-03-06'
title='US court sides with Apple, Tesla, other tech companies over child labor in Africa', updated_time='2024-03-06'
Data has been successfully saved to tesla_news.csv
title='Podcast: A Vermont victory wasn't enough for Haley, and other top stories', updated_time='2024-03-07'
title='BYD deepens EV price war in China with latest cut for its cheapest car', updated_time='2024-03-06'
title='Trump meets with Musk and Republican donors in Florida, NYT reports', updated_time='2024-03-06'
title='Tech-heavy Nasdaq leads Wall Street lower as megacaps, chips slide', updated_time='2024-03-06'
title='Senator Rubio seeks stiffer tariff to stop China 'flooding US auto markets'', updated_time='2024-03-05'
title='Canadian dollar edges lower as Wall Street slides', updated_time='2024-03-05'
title='Could the $6 billion legal fee in Musk's Tesla pay case be reasonable?', updated_time='2024-03-05'
title='The 'Volcano group' claiming the arson near Tesla's German plant', updated_time='2024-03-05'
title='Tesla Germany halts work as Musk calls suspected arson 'extremely dumb'', updated_time='2024-03-05'
title='Letter claims Tesla fire in name of militant leftist group', updated_time='2024-03-05'
title='E.ON says power restored to area after Tesla forced to halt production', updated_time='2024-03-05'
title='Morning Bid: Apple angst, Tesla tanks, Bitcoin balks', updated_time='2024-03-05'
title='Volvo Cars posts 2% slide in February sales volume', updated_time='2024-03-05'
title='Korea's Kia, Thailand in talks over building new EV facility, sources say', updated_time='2024-03-05'
title='China aims for self-reliance in tech; vows to open manufacturing to foreign investors', updated_time='2024-03-05'
title='Tesla shares skid after China sales fell to the lowest level in over a year', updated_time='2024-03-05'
title='Tesla shareholders' bid for $6 bln in legal fees over Musk pay could fan flames for Delaware critics', updated_time='2024-03-04'
title='China's BYD prices new version of best-selling EV lower than predecessor', updated_time='2024-03-04'
title='Gravity opens fastest EV charger in US to public in New York City', updated_time='2024-03-04'
title=''Critical' Thai economy needs urgent stimulus, says PM's office', updated_time='2024-03-04'
Data has been successfully saved to tesla_news.csv
title='Tesla in talks with Thailand for production facility, says government official', updated_time='2024-03-04'
title='Exclusive: VW battery unit rules out IPO until factories running, unified cell in use', updated_time='2024-03-04'
title='Lawyers who voided Elon Musk's pay as excessive want $6 billion fee', updated_time='2024-03-03'
title='Elon Musk sues OpenAI for abandoning original mission for profit', updated_time='2024-03-01'
title='Nvidia closes with $2 trillion valuation as Dell stokes AI rally', updated_time='2024-03-01'
title='Exclusive: Nissan, Fisker in advanced talks on investment, partnership-sources', updated_time='2024-03-01'
title='The politics and economics behind Biden's China-car espionage probe', updated_time='2024-03-01'
title='US to probe if Chinese cars pose national data security risks', updated_time='2024-03-01'
title='Volkswagen expects slower sales growth as economic outlook dims', updated_time='2024-03-01'
title='Tesla rolls out new incentives in China as price war escalates', updated_time='2024-03-01'
title='China's Li Auto pins hopes on first all-electric model', updated_time='2024-03-01'
title='Tesla raises prices of some Model Y vehicles in US', updated_time='2024-03-01'
title='Nasdaq notches first record high close since 2021', updated_time='2024-03-01'
title='US judge signals Elon Musk's X may lose case against hate speech watchdog', updated_time='2024-02-29'
title='Automakers question feasibility of California 2035 EV sales mandate plan', updated_time='2024-02-29'
title='OpenAI wins order blocking tech entrepreneur's 'Open AI' website', updated_time='2024-02-29'
title='Bridgewater founder Dalio calls Magnificent Seven a 'bit frothy'', updated_time='2024-02-29'
title='Tesla must face race bias class action by 6,000 Black US workers', updated_time='2024-02-29'
title='Robotics startup Figure raises $675 mln from Microsoft, Nvidia, OpenAI', updated_time='2024-02-29'
title='Apple picks a better road to AI dominance', updated_time='2024-03-02'
Data has been successfully saved to tesla_news.csv
title='Volkswagen, Xpeng say first jointly made car will be electric SUV', updated_time='2024-02-29'
title='Exclusive: US law on domestic abuse should cover carmakers, FCC chair says', updated_time='2024-02-28'
title='Chinese automaker BYD looking for Mexico plant location, executive says', updated_time='2024-02-28'
title='Republican US senator wants to hike tariffs on Chinese vehicles', updated_time='2024-02-28'
title='EU lawmakers back draft rules on patents for connected cars, telecom equipment', updated_time='2024-02-28'
title='Polestar raises almost $1 bln, sees margins improving in 2024', updated_time='2024-02-28'
title='Italy talks to Tesla, Chinese carmakers in search for second auto producer', updated_time='2024-02-28'
title='Apple cancels decade-long electric car project, source says', updated_time='2024-02-28'
title='BYD amps up brutal China EV price war with bigger discounts on new car versions', updated_time='2024-02-28'
title='Tesla aims to ship Roadster cars next year, Musk says', updated_time='2024-02-28'
title='Siblings help China's BYD grab early lead in Thai EV market', updated_time='2024-02-27'
title='UAW says majority of workers at Mercedes Benz US plant seek to join union', updated_time='2024-02-27'
title='Hybrids, gas vehicles fuel legacy automakers' shares past EV rivals', updated_time='2024-02-27'
title='Electrified vehicles are becoming more popular in US, survey says', updated_time='2024-02-27'
title='Renault leads Europe's EV defence in Geneva as Chinese rivals expand', updated_time='2024-02-27'
title='Nvidia bets dominate US options market as AI fervor grows', updated_time='2024-02-26'
title='Amazon's AWS to invest over $5 bln to boost cloud computing in Mexico', updated_time='2024-02-26'
title='Facing Chinese EV rivals, Europe's automakers squeeze suppliers on costs', updated_time='2024-02-26'
title='Vietnam's VinFast seeks India EV import duty cut as plant construction starts', updated_time='2024-02-25'
title='US should block cheap Chinese auto imports from Mexico, US makers say', updated_time='2024-02-24'
Data has been successfully saved to tesla_news.csv
title='S&P, Dow eke out another record closing high as Nvidia momentum endures', updated_time='2024-02-23'
title='Tesla to fix software in 8,700 vehicles in China product recall, regulator says', updated_time='2024-02-23'
title='Rivian swerves dangerously into crowded slow lane', updated_time='2024-02-23'
title='Red Sea ship attacks not driving inflation, Moody's says', updated_time='2024-02-22'
title='Rivian, Lucid tumble as slowing EV demand upsets ramp-up plans', updated_time='2024-02-22'
title='EV sector grapples with layoffs, production cuts and altered plans', updated_time='2024-02-22'
title='VinFast loss widens in Q4, confident of achieving 2024 sales goals', updated_time='2024-02-22'
title='UAW commits $40 million to organizing US auto, EV battery workers', updated_time='2024-02-22'
title='Elon Musk gets some guidance from a Delaware judge on how to ditch the state', updated_time='2024-02-22'
title='Chinese electric truck start-up Windrose explores production in Europe, CEO says', updated_time='2024-02-22'
title='Rivian, Lucid's 2024 production targets disappoint as EV demand wanes', updated_time='2024-02-22'
title='Nvidia stock surges as revenue forecast tops estimates, AI demand continues', updated_time='2024-02-22'
title='Nvidia stock surges after results, lifts other AI shares', updated_time='2024-02-21'
title='Another Swedish union warns of action against Tesla', updated_time='2024-02-21'
title='Morning Bid: Nvidia chipped while Fed minutes pass', updated_time='2024-02-21'
title='Morning Bid: Markets wait on Nvidia, Fed minutes', updated_time='2024-02-21'
title='Stocks close lower as Nvidia weighs ahead of earnings', updated_time='2024-02-20'
title='Nvidia dethrones Tesla as Wall Street's most traded stock', updated_time='2024-02-20'
title='Tesla faces rejection from locals for plans to expand German site', updated_time='2024-02-20'
title='TripAdvisor can ditch Delaware in case Elon Musk highlighted', updated_time='2024-02-20'
Data has been successfully saved to tesla_news.csv
title='Will Smith-backed Arc launches mass-market electric sport boat for $258,000', updated_time='2024-02-20'
title='US to soften tailpipe rules, slow EV transition through 2030', updated_time='2024-02-20'
title='Eli Lilly, Novo Nordisk get growth stock status on weight-loss drug boost', updated_time='2024-02-20'
title='How Elon Musk's SpaceX was boosted in Indonesia by a Chinese rocket failure', updated_time='2024-02-20'
title='EU new car sales rise 12% y/y in January - ACEA', updated_time='2024-02-20'
title='Reuters wins Polk Award for coverage of Elon Musk’s business empire', updated_time='2024-02-19'
title='Chinese EV maker Xpeng to hire 4,000, invest in AI', updated_time='2024-02-19'
title='Wall St Week Ahead High-flying Nvidia’s earnings could test US stock market’s AI dreams', updated_time='2024-02-18'
title='Mexico's peso as an uber-dollar', updated_time='2024-02-16'
title='Australia's nickel producers face writedowns amid supply glut', updated_time='2024-02-16'
title='Delaware's business court to lose long-serving judge', updated_time='2024-02-15'
title='European, US carmakers race to lower EVs costs as China competition heats up', updated_time='2024-02-15'
title='Stellantis can be an industry 'consolidator', CFO says', updated_time='2024-02-15'
title='Biden's drive for EVs collides with Detroit's profit machines', updated_time='2024-02-15'
title='Lucid slashes prices of Air luxury EVs again to spur demand', updated_time='2024-02-15'
title='BHP flags $5.7 bln impairments on Samarco dam failure, nickel operations', updated_time='2024-02-15'
title='Swedish union to exempt some Tesla cars from mechanic strike', updated_time='2024-02-15'
title='Musk says SpaceX has moved its incorporation to Texas from Delaware', updated_time='2024-02-15'
title='Wall Street ends higher, lifted by Uber, Lyft and Nvidia', updated_time='2024-02-14'
title='Musk, Tesla shareholder to propose a stay of pay ruling during appeal', updated_time='2024-02-14'
Data has been successfully saved to tesla_news.csv
title='Despite weak lithium prices, Albemarle beats on cost cuts', updated_time='2024-02-14'
title='Bridgewater increased Nvidia stake more than 450% in Q4 -filings', updated_time='2024-02-14'
title='Renault posts lower-than-expected FY profit, ups dividend', updated_time='2024-02-14'
title='US auto retailers' falling new vehicle margins compress quarterly profits', updated_time='2024-02-14'
title='Explainer: Did Delaware 'lock the doors' to stop companies from leaving, as Musk claims?', updated_time='2024-02-14'
title='US issues $135 million in advance EV tax rebates since Jan 1, Treasury says', updated_time='2024-02-14'
title='Sony, Honda EV venture to roll out three models before 2030 - Nikkei', updated_time='2024-02-14'
title='US markets grapple with mysterious contradictions', updated_time='2024-02-14'
title='OpenAI researcher Andrej Karpathy departs firm', updated_time='2024-02-14'
title='China's BYD plans new electric vehicle plant in Mexico, says Nikkei', updated_time='2024-02-14'
title='Elon Musk asks Texas court to bar deposition in defamation lawsuit', updated_time='2024-02-13'
title='US lawmakers urge Delta Air to remain neutral in union organizing', updated_time='2024-02-13'
title='German labour court finds Tesla broke union election rules', updated_time='2024-02-13'
title='Tata Motors cuts EV prices by 1%-8%, first by an Indian carmaker', updated_time='2024-02-13'
title='Morning Bid: Nikkei and Nasdaq stalk records, CPI lurks', updated_time='2024-02-13'
title='Robot invasion hit a bump in 2023 as North American economy cooled', updated_time='2024-02-12'
title='US judge orders Elon Musk to testify in SEC's Twitter probe', updated_time='2024-02-12'
title='France halts subsidised EV lease programme after strong demand', updated_time='2024-02-12'
title='Tesla temporarily slashes prices for some Model Y cars in the US', updated_time='2024-02-10'
title='Wall St Week Ahead: Market breadth suggests narrowing rally as S&P 500 hits records', updated_time='2024-02-09'
Data has been successfully saved to tesla_news.csv
title='Musk's Neuralink switches location of incorporation to Nevada', updated_time='2024-02-09'
title='Tesla supplier Panasonic secures Novonix's graphite for EV battery', updated_time='2024-02-08'
title='Elon Musk's X taps small D.C. law firm for free-speech battles', updated_time='2024-02-08'
title='Tesla trails Ford and GM in revenue generated per worker', updated_time='2024-02-08'
title='Volkswagen sticking with North American EV plans - executive', updated_time='2024-02-08'
title='Directors and executives take note: Delaware court voids Elon Musk's $55B Tesla pay package', updated_time='2024-02-08'
title='GM appoints former Tesla executive as VP of battery unit', updated_time='2024-02-08'
title='LG Energy plans to sell batteries to more EV car makers in India', updated_time='2024-02-08'
title='China's passenger vehicle sales fall 14.1% in Jan m/m', updated_time='2024-02-08'
title='Tesla asks which jobs are critical, stoking layoff fears - Bloomberg News', updated_time='2024-02-08'
title='UAW says majority of workers at VW Tennessee plant seek to join union', updated_time='2024-02-07'
title='A pinch of salt could unsettle electric-car order', updated_time='2024-02-07'
title='China's new energy vehicle sales drop m/m for first time since Aug', updated_time='2024-02-07'
title='Ford slows EVs, sends a truckload of cash to investors', updated_time='2024-02-07'
title='Tesla's German plant produced 6,000 cars in a week -Tagesspiegel', updated_time='2024-02-07'
title='Fired 'Mandalorian' actor sues Disney with funding from Elon Musk', updated_time='2024-02-06'
title='Hertz shares regain traction after wider-than-expected Q4 loss', updated_time='2024-02-06'
title='SpaceX probed by California over sex bias, retaliation claims', updated_time='2024-02-06'
title='India needs less Tesla, more Toyota', updated_time='2024-02-06'
title='Analysis: Will shakeup at net-zero targets arbiter be enough to quiet its critics?', updated_time='2024-02-14'
Data has been successfully saved to tesla_news.csv
title='Red Sea delays are no panacea for European EVs', updated_time='2024-02-06'
title='Wall Street equities fall with interest rates, earnings in focus', updated_time='2024-02-06'
title='Tesla falls on SAP snub report, Piper Sandler price target cut', updated_time='2024-02-05'
title='Viral videos of Tesla drivers using VR headsets prompt US government alarm', updated_time='2024-02-05'
title='Hyundai explores $3 billion IPO for India unit at up to $30 billion valuation, sources say', updated_time='2024-02-05'
title=''Magnificent Seven' continued market dominance rests on sales growth, Goldman says', updated_time='2024-02-05'
title='Onsemi beats fourth-quarter estimates on strong chip demand', updated_time='2024-02-05'
title='SAP removes Tesla from list of company car suppliers -Handelsblatt', updated_time='2024-02-05'
title='Toyota to outshine rivals as more consumers opt for hybrids amid EV slowdown', updated_time='2024-02-04'
title='Fourth-quarter US earnings estimate improving quickly', updated_time='2024-02-02'
title='U.S. safety agency deepens probe of Tesla power steering loss', updated_time='2024-02-02'
title='Elon Musk charts largely painless Delaware exodus', updated_time='2024-02-02'
title='Spotify signs new deal with popular US podcaster Joe Rogan', updated_time='2024-02-02'
title='Farmers block Dutch-Belgian border as protests spread across Europe', updated_time='2024-02-02'
title='Tesla agrees to pay $1.5 million to settle California hazardous waste lawsuit', updated_time='2024-02-02'
title='Tesla woes get investors talking about its successor in 'Magnificent Seven'', updated_time='2024-02-02'
title='Tesla activist investors to seize on Elon Musk pay ruling', updated_time='2024-02-02'
title='Want details on Elon Musk's brain implant trial? You'll have to ask him', updated_time='2024-02-02'
title='US agency upgrades probe into Tesla's power steering loss into engineering analysis', updated_time='2024-02-02'
title='Panasonic's battery unit sees Q3 profit rise on stronger North America sales', updated_time='2024-02-02'
Data has been successfully saved to tesla_news.csv
title='Nvidia sets monthly record with unprecedented market value surge in January', updated_time='2024-02-02'
title='Volvo's Polestar troubles signal 'shakeout time' for EV industry', updated_time='2024-02-02'
title='Morning Bid: Markets bask in afterglow of tech outperformers', updated_time='2024-02-02'
title='Musk seeks Tesla shareholder vote on moving incorporation to Texas', updated_time='2024-02-01'
title='Elon Musk bashed by heavy metal drummer who cost him $56 billion', updated_time='2024-02-01'
title='Tesla opens first South America store in Chilean capital', updated_time='2024-02-01'
title='Shifting Tesla incorporation to Texas may not give Musk what he wants', updated_time='2024-02-01'
title='Wall St Week Ahead Fed, earnings and economic data to test US stocks near record highs', updated_time='2024-02-01'
title='Big Tech move into finance to come under closer EU scrutiny', updated_time='2024-02-01'
title='Human Rights Watch warns carmakers over China forced labour risk', updated_time='2024-02-01'
title='German dealership Sternauto opens BYD store in Berlin', updated_time='2024-02-01'
title='Volvo Cars to stop funding Polestar, may hand stake to Geely', updated_time='2024-02-01'
title='Morning Bid: For markets, a Fed delay means more in May', updated_time='2024-02-01'
title='Tesla China offers cash discounts for some Model Y types', updated_time='2024-02-01'
title='Elon Musk's 'unfathomable' $56 billion Tesla pay package', updated_time='2024-02-01'
title='Musk, Tesla board have to start from scratch after $56 billion pay deal thrown out', updated_time='2024-01-31'
title='Tesla to bring LFP battery supply chain to US - Bloomberg News', updated_time='2024-01-31'
title='Tesla sued by California counties over hazardous waste', updated_time='2024-01-31'
title='Judge voids Elon Musk's 'unfathomable' $56 billion Tesla pay package', updated_time='2024-01-31'
title='Megacap stocks keep lifting US market, but worries over their dominance grow', updated_time='2024-01-31'
Data has been successfully saved to tesla_news.csv
title='New US ETF targets 'Magnificent Seven' mega-cap technology stocks', updated_time='2024-01-31'
title='Battery minerals pressure causes cracks Down Under', updated_time='2024-01-31'
title='Morning Bid: Megacaps stumble at high bar as Fed decision awaited', updated_time='2024-01-31'
title='What investors and legal experts are saying about Elon Musk's voided $56 bln pay package', updated_time='2024-01-31'
title='China EV battery maker CATL expects weaker earnings growth for 2023', updated_time='2024-01-31'
title='Who is Kathaleen McCormick, the judge who slashed Elon Musk's pay package?', updated_time='2024-01-30'
title='Explainer: The case against Elon Musk's $56 billion pay package', updated_time='2024-01-30'
title='Stocks meander, yields mixed after strong US labor data', updated_time='2024-01-30'
title='Tesla California registrations fall for the first time since the pandemic', updated_time='2024-01-30'
title='Industry pain abounds as electric car demand hits slowdown', updated_time='2024-01-30'
title='GM shares jump as CEO Barra promises shareholders more cash', updated_time='2024-01-30'
title='China's BYD forecasts 2023 net profit to rise as much as 86.5% y/y', updated_time='2024-01-30'
title='Albemarle lays off 4% of its global workforce -The Information', updated_time='2024-01-29'
title='Stocks surge, bond yields slip ahead of Fed decision', updated_time='2024-01-29'
title='S&P 500 hits fresh closing high ahead of Fed meeting, big tech earnings', updated_time='2024-01-29'
title='GM looks for a road out of Tesla's shadow', updated_time='2024-01-29'
title='Weak Intel forecast drags S&P 500, ends streak of record highs', updated_time='2024-01-26'
title='Elon Musk says his AI startup xAI not raising capital', updated_time='2024-01-26'
title='Tesla to recall 200,000 vehicles in US to fix rearview glitch - NHTSA', updated_time='2024-01-26'
title='Electric car maker Polestar to cut around 450 jobs globally', updated_time='2024-01-26'
Data has been successfully saved to tesla_news.csv

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-2-a32ca08cc2a2> in <cell line: 23>()
     23 for page in range(num_pages):
     24     url = f'https://www.reuters.com/pf/api/v3/content/fetch/articles-by-search-v2?query=%7B%22keyword%22%3A%22tesla%22%2C%22offset%22%3A{offset}%2C%22orderby%22%3A%22display_date%3Adesc%22%2C%22size%22%3A20%2C%22website%22%3A%22reuters%22%7D&d=190&_website=reuters'
---> 25     soup = json.loads(get_content(url))
     26     articles_info = [(article['title'], article['updated_time']) for article in soup['result']['articles']]
     27 

<ipython-input-2-a32ca08cc2a2> in get_content(url)
     15     return soup
     16 def get_content(url):
---> 17     content=requests.get(url).content
     18     soup = BeautifulSoup(content, 'html.parser')
     19     return soup.text

/usr/local/lib/python3.10/dist-packages/requests/api.py in get(url, params, **kwargs)
     71     """
     72 
---> 73     return request("get", url, params=params, **kwargs)
     74 
     75 

/usr/local/lib/python3.10/dist-packages/requests/api.py in request(method, url, **kwargs)
     57     # cases, and look like a memory leak in others.
     58     with sessions.Session() as session:
---> 59         return session.request(method=method, url=url, **kwargs)
     60 
     61 

/usr/local/lib/python3.10/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
    587         }
    588         send_kwargs.update(settings)
--> 589         resp = self.send(prep, **send_kwargs)
    590 
    591         return resp

/usr/local/lib/python3.10/dist-packages/requests/sessions.py in send(self, request, **kwargs)
    701 
    702         # Send the request
--> 703         r = adapter.send(request, **kwargs)
    704 
    705         # Total elapsed time of the request (approximately)

/usr/local/lib/python3.10/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
    484 
    485         try:
--> 486             resp = conn.urlopen(
    487                 method=request.method,
    488                 url=url,

/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    789 
    790             # Make the request on the HTTPConnection object
--> 791             response = self._make_request(
    792                 conn,
    793                 method,

/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    535         # Receive the response from the server
    536         try:
--> 537             response = conn.getresponse()
    538         except (BaseSSLError, OSError) as e:
    539             self._raise_timeout(err=e, url=url, timeout_value=read_timeout)

/usr/local/lib/python3.10/dist-packages/urllib3/connection.py in getresponse(self)
    459 
    460         # Get the response from http.client.HTTPConnection
--> 461         httplib_response = super().getresponse()
    462 
    463         try:

/usr/lib/python3.10/http/client.py in getresponse(self)
   1373         try:
   1374             try:
-> 1375                 response.begin()
   1376             except ConnectionError:
   1377                 self.close()

/usr/lib/python3.10/http/client.py in begin(self)
    316         # read until we get a non-100 response
    317         while True:
--> 318             version, status, reason = self._read_status()
    319             if status != CONTINUE:
    320                 break

/usr/lib/python3.10/http/client.py in _read_status(self)
    277 
    278     def _read_status(self):
--> 279         line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
    280         if len(line) > _MAXLINE:
    281             raise LineTooLong("status line")

/usr/lib/python3.10/socket.py in readinto(self, b)
    703         while True:
    704             try:
--> 705                 return self._sock.recv_into(b)
    706             except timeout:
    707                 self._timeout_occurred = True

/usr/lib/python3.10/ssl.py in recv_into(self, buffer, nbytes, flags)
   1301                   "non-zero flags not allowed in calls to recv_into() on %s" %
   1302                   self.__class__)
-> 1303             return self.read(nbytes, buffer)
   1304         else:
   1305             return super().recv_into(buffer, nbytes, flags)

/usr/lib/python3.10/ssl.py in read(self, len, buffer)
   1157         try:
   1158             if buffer is not None:
-> 1159                 return self._sslobj.read(len, buffer)
   1160             else:
   1161                 return self._sslobj.read(len)

KeyboardInterrupt:


import re
import requests
from bs4 import BeautifulSoup
import json
import pandas as pd
from datetime import datetime


df = pd.read_csv('tesla_news.csv')
df.head()


df.dtypes

title           object
updated_time    object
dtype: object


df.shape

(5459, 2)


# Convert "Published Date" to datetime format
df['updated_time'] = pd.to_datetime(df['updated_time'])
df.dtypes

title                   object
updated_time    datetime64[ns]
dtype: object


# Remove repeated rows
df = df.drop_duplicates()


df.shape

(5445, 2)


df.isnull().sum()

title           0
updated_time    0
dtype: int64


import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.

True


import re
import matplotlib.pyplot as plt
from nltk.corpus import stopwords
from wordcloud import WordCloud
from collections import Counter
from nltk.tokenize import word_tokenize

# Function to plot frequent punctuation marks
def plot_punctuation_frequency(text, top_n=20):
    # Find all punctuation marks
    punctuation = re.findall(r'[^\w\s]', text)
    # Count the frequency of each punctuation mark
    punctuation_count = Counter(punctuation)
    # Get the most common punctuation marks
    most_common_punctuation = punctuation_count.most_common(top_n)
    # Unpack the keys and values for plotting
    keys, values = zip(*most_common_punctuation)
    # Plot the frequency with bright colors
    plt.figure(figsize=(18, 6))
    plt.bar(keys, values, color='skyblue')
    plt.xlabel('Punctuation Marks', fontsize=14)
    plt.ylabel('Frequency', fontsize=14)
    plt.title(f'Top {top_n} Most Frequent Punctuation Marks', fontsize=16)
    plt.xticks()
    plt.yticks()
    plt.show()

# Function to plot frequent stop words
def plot_stopwords_frequency(text, top_n=20):
    # Tokenize the text
    tokens = word_tokenize(text)
    # Find stop words
    stop_words = set(stopwords.words('english'))
    stop_words_found = [word for word in tokens if word in stop_words]
    # Count the frequency of each stop word
    stop_words_count = Counter(stop_words_found)
    # Get the most common stop words
    most_common_stopwords = stop_words_count.most_common(top_n)
    # Unpack the keys and values for plotting
    keys, values = zip(*most_common_stopwords)
    # Plot the frequency with bright colors
    plt.figure(figsize=(18, 6))
    plt.bar(keys, values, color='purple')
    plt.xlabel('Stop Words', fontsize=14)
    plt.ylabel('Frequency', fontsize=14)
    plt.title(f'Top {top_n} Most Frequent Stop Words', fontsize=16)
    plt.xticks(rotation=45)
    plt.yticks()
    plt.show()

# Function to generate word cloud with bright colors
def generate_word_cloud(text):
    wordcloud = WordCloud(width=1200, height=600,
                          background_color='white',
                          colormap='viridis',
                          stopwords=set(stopwords.words('english')),
                          min_font_size=10).generate(text)
    plt.figure(figsize=(10, 6), facecolor=None)
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis("off")
    plt.tight_layout(pad=0)
    plt.show()


plot_punctuation_frequency(df['title'].str.cat(sep=' '))


# Visualize stop words frequency
text = ' '.join(df['title'])
plot_stopwords_frequency(text)


# Generate word cloud
generate_word_cloud(text)


from nltk import pos_tag
from nltk.stem import WordNetLemmatizer

# Preprocessing function
def preprocess_text(text):
    # Convert to lowercase
    text = text.lower()
    # Remove punctuation
    text = re.sub(r'[^\w\s]', '', text)
    # Remove digits
    text = re.sub(r'\d+', '', text)
    # Tokenize the text
    tokens = word_tokenize(text)
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [word for word in tokens if word not in stop_words]
    # POS tagging
    pos_tags = pos_tag(filtered_tokens)
    # Lemmatization
    lemmatizer = WordNetLemmatizer()
    lemmatized_text = []
    for word, tag in pos_tags:
        if tag.startswith('NN'):  # Noun
            lemma = lemmatizer.lemmatize(word, pos='n')
        elif tag.startswith('VB'):  # Verb
            lemma = lemmatizer.lemmatize(word, pos='v')
        elif tag.startswith('JJ'):  # Adjective
            lemma = lemmatizer.lemmatize(word, pos='a')
        else:
            lemma = word
        lemmatized_text.append(lemma)
    # Join the lemmatized tokens back into a single string
    preprocessed_text = ' '.join(lemmatized_text)
    return preprocessed_text

# Apply preprocessing to 'Title' and 'Content' columns
df['title'] = df['title'].apply(preprocess_text)

# Display the preprocessed DataFrame
df


# Generate word cloud
text = ' '.join(df['title'])
generate_word_cloud(text)


# Function to clean tweets
def clean_news(news):
    # Remove 'tsla' and 'tesla' (case-insensitive)
    tweet = re.sub(r'\b(tsla|tesla)\b', '', news, flags=re.IGNORECASE)
    # Remove extra spaces
    tweet = re.sub(r'\s+', ' ', news).strip()
    return tweet

# Apply the cleaning function to the 'Tweet' column
df['title'] = df['title'].apply(clean_news)

# Display the modified dataframe
df


# Generate word cloud
text = ' '.join(df['title'])
generate_word_cloud(text)


df.shape

(5445, 2)


df.to_csv('news_tesla.csv', index=False)


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


df = pd.read_csv('preprocessed_tweets.csv')
df1 = pd.read_csv('news_tesla.csv')


print(df.dtypes)
print(df1.dtypes)

Date             object
Tweet            object
Cleaned Tweet    object
dtype: object
title           object
updated_time    object
dtype: object


df1.rename(columns={'updated_time': 'Date', 'title': 'News'}, inplace=True)


pip install vaderSentiment

Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.10/dist-packages (3.3.2)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from vaderSentiment) (2.31.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (2024.2.2)


from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import pandas as pd

# Initialize the VADER sentiment analyzer
analyzer = SentimentIntensityAnalyzer()

# Ensure all entries in 'Cleaned Tweet' column are strings
df['Cleaned Tweet'] = df['Cleaned Tweet'].astype(str)

# Apply VADER sentiment analysis on the tweets data with 'Date' and 'Cleaned Tweet' columns
df['Tweet Sentiment'] = df['Cleaned Tweet'].apply(lambda text: analyzer.polarity_scores(text)['compound'])

neutral_threshold = 0.0

# Function to label sentiment scores
def label_sentiment(score):
    if score == neutral_threshold:
        return 'Neutral'
    elif score > neutral_threshold:
        return 'Positive'
    else:
        return 'Negative'

# Apply labeling to the 'Tweet Sentiment' column
df['Tweet Sentiment Label'] = df['Tweet Sentiment'].apply(label_sentiment)

# Display the resulting dataframe with tweet sentiment scores
df[['Date', 'Cleaned Tweet', 'Tweet Sentiment', 'Tweet Sentiment Label']]


# Apply VADER sentiment analysis on the news data with 'Date' and 'News' columns
df1['News Sentiment'] = df1['News'].apply(lambda text: analyzer.polarity_scores(text)['compound'])

neutral_threshold = 0.0

# Function to label sentiment scores
def label_sentiment(score):
    if score == neutral_threshold:
        return 'Neutral'
    elif score > neutral_threshold:
        return 'Positive'
    else:
        return 'Negative'

# Apply labeling to the 'News Sentiment' column
df1['News Sentiment Label'] = df1['News Sentiment'].apply(label_sentiment)

# Display the resulting dataframe with news sentiment scores
df1[['Date', 'News', 'News Sentiment', 'News Sentiment Label']]


import pandas as pd
import numpy as np
import re
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk import pos_tag
from nltk.stem import WordNetLemmatizer


# Load the tweets dataset from CSV
tweets_labelled_df = pd.read_csv('Labelled Tweets.csv')

tweets_labelled_df.head()


import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!

True


# Function to preprocess the text
def preprocess_text(text):
    # Remove mentions (@), hashtags (#), and URLs
    text = re.sub(r'@\w+', '', text)
    text = re.sub(r'#\w+', '', text)
    text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
    # Remove punctuation
    text = re.sub(r'[^\w\s]', '', text)
    # Convert to lower case
    text = text.lower()
    # Remove digits
    text = re.sub(r'\d+', '', text)
    # Tokenize the text
    tokens = word_tokenize(text)
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [word for word in tokens if word not in stop_words]
    # POS tagging
    pos_tags = pos_tag(filtered_tokens)
    # Lemmatization
    lemmatizer = WordNetLemmatizer()
    lemmatized_text = []
    for word, tag in pos_tags:
        if tag.startswith('NN'):  # Noun
            lemma = lemmatizer.lemmatize(word, pos='n')
        elif tag.startswith('VB'):  # Verb
            lemma = lemmatizer.lemmatize(word, pos='v')
        elif tag.startswith('JJ'):  # Adjective
            lemma = lemmatizer.lemmatize(word, pos='a')
        else:
            lemma = word
        lemmatized_text.append(lemma)
    # Join the lemmatized tokens back into a single string
    preprocessed_text = ' '.join(lemmatized_text)
    return preprocessed_text
    return text

# Apply preprocessing to 'full_text' column
tweets_labelled_df['cleaned_text'] = tweets_labelled_df['full_text'].apply(preprocess_text)


from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import make_pipeline

# Convert 'score' to numeric
tweets_labelled_df['score'] = pd.to_numeric(tweets_labelled_df['score'], errors='coerce')

# Drop rows with missing values
tweets_labelled_df.dropna(inplace=True)

# Split the dataset into training and testing sets
train_data, test_data, train_labels, test_labels = train_test_split(tweets_labelled_df['full_text'], \
                                                                   tweets_labelled_df['score'], \
                                                                    test_size=0.2, random_state=42)

# Build a pipeline with a CountVectorizer and a Linear Regression model
model = make_pipeline(CountVectorizer(), LinearRegression())

# Train the model
model.fit(train_data, train_labels)

Pipeline(steps=[('countvectorizer', CountVectorizer()),
                ('linearregression', LinearRegression())])

Pipeline(steps=[('countvectorizer', CountVectorizer()),
                ('linearregression', LinearRegression())])

CountVectorizer()

LinearRegression()


# Apply the model to the tweets dataset
df['Custom Tweets Sentiment Scores'] = model.predict(df['Tweet'])

# Define thresholds for sentiment categories
def categorize_sentiment(score):
    if score > 0:
        return 'positive'
    elif score < 0:
        return 'negative'
    else:
        return 'neutral'

# Apply labeling to the predicted scores
df['Custom Tweets Sentiment Labels'] = df['Custom Tweets Sentiment Scores'].apply(categorize_sentiment)

df[['Date', 'Cleaned Tweet', 'Custom Tweets Sentiment Scores', 'Custom Tweets Sentiment Labels']]


import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import make_pipeline

# Load the news sentiment dataset from CSV
news_labelled_df = pd.read_csv('reliance_news_sentiment.csv')
news_labelled_df.head()


# Preprocess the news sentiment dataset
def preprocess_text(text):
    # Convert to lowercase
    text = text.lower()
    # Remove punctuation
    text = re.sub(r'[^\w\s]', '', text)
    # Remove digits
    text = re.sub(r'\d+', '', text)
    # Tokenize the text
    tokens = word_tokenize(text)
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [word for word in tokens if word not in stop_words]
    # POS tagging
    pos_tags = pos_tag(filtered_tokens)
    # Lemmatization
    lemmatizer = WordNetLemmatizer()
    lemmatized_text = []
    for word, tag in pos_tags:
        if tag.startswith('NN'):  # Noun
            lemma = lemmatizer.lemmatize(word, pos='n')
        elif tag.startswith('VB'):  # Verb
            lemma = lemmatizer.lemmatize(word, pos='v')
        elif tag.startswith('JJ'):  # Adjective
            lemma = lemmatizer.lemmatize(word, pos='a')
        else:
            lemma = word
        lemmatized_text.append(lemma)
    # Join the lemmatized tokens back into a single string
    preprocessed_text = ' '.join(lemmatized_text)
    return preprocessed_text

# Apply preprocessing to 'title' column
news_labelled_df['title'] = news_labelled_df['title'].apply(preprocess_text)

# Split the dataset into training and testing sets
train_data, test_data, train_labels, test_labels = train_test_split(news_labelled_df['title'], \
                                                                    news_labelled_df['sentiment_score'],\
                                                                          test_size=0.2, random_state=42)

# Build a pipeline with a CountVectorizer and a Linear Regression model
model = make_pipeline(CountVectorizer(), LinearRegression())

# Train the model
model.fit(train_data, train_labels)

Pipeline(steps=[('countvectorizer', CountVectorizer()),
                ('linearregression', LinearRegression())])

Pipeline(steps=[('countvectorizer', CountVectorizer()),
                ('linearregression', LinearRegression())])

CountVectorizer()

LinearRegression()


# Apply the model to the news dataset
df1['Custom News Sentiment Scores'] = model.predict(df1['News'])

# Define thresholds for sentiment categories
def categorize_sentiment(score):
    if score > 0:
        return 'positive'
    elif score < 0:
        return 'negative'
    else:
        return 'neutral'

# Apply labeling to the predicted scores
df1['Custom News Sentiment Labels'] = df1['Custom News Sentiment Scores'].apply(categorize_sentiment)

df1[['Date', 'News', 'Custom News Sentiment Scores', 'Custom News Sentiment Labels']]


from textblob import TextBlob

def get_sentiment_textblob(text):
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity
    if polarity > 0:
        return 'Positive', polarity
    elif polarity < 0:
        return 'Negative', polarity
    else:
        return 'Neutral', polarity


# Apply sentiment lexicon analysis on tweets
df['TB Tweets Sentiment Labels'], df['TB Tweets Sentiment'] = \
    zip(*df['Cleaned Tweet'].apply(get_sentiment_textblob))
df


# Apply sentiment lexicon analysis on tweets
df1['TB News Sentiment Labels'], df1['TB News Sentiment'] = zip(*df1['News'].apply(get_sentiment_textblob))
df1


import matplotlib.pyplot as plt

# Create subplots for the first three pie charts
fig, axes = plt.subplots(1, 3, figsize=(18, 6))

# Calculate sentiment percentages for each approach
vader_sentiment_counts = df['Tweet Sentiment Label'].value_counts(normalize=True) * 100
custom_sentiment_counts = df['Custom Tweets Sentiment Labels'].value_counts(normalize=True) * 100
textblob_sentiment_counts = df['TB Tweets Sentiment Labels'].value_counts(normalize=True) * 100

# Plot the sentiment percentages as pie charts
axes[0].pie(vader_sentiment_counts, labels=vader_sentiment_counts.index, autopct='%1.1f%%', \
            colors=['skyblue', 'lightgreen', 'lightcoral'])
axes[0].set_title('Sentiment Analysis Results for Tweets (VADER)')

axes[1].pie(custom_sentiment_counts, labels=custom_sentiment_counts.index, autopct='%1.1f%%', \
            colors=['skyblue', 'lightgreen', 'lightcoral'])
axes[1].set_title('Sentiment Analysis Results for Tweets (Custom Model)')

axes[2].pie(textblob_sentiment_counts, labels=textblob_sentiment_counts.index, autopct='%1.1f%%',\
             colors=['skyblue', 'lightgreen', 'lightcoral'])
axes[2].set_title('Sentiment Analysis Results for Tweets (TextBlob)')

plt.show()


import matplotlib.pyplot as plt

# Create subplots for the pie charts
fig, axes = plt.subplots(1, 3, figsize=(18, 6))

# Calculate sentiment percentages for each approach
vader_sentiment_counts_news = df1['News Sentiment Label'].value_counts(normalize=True) * 100
custom_sentiment_counts_news = df1['Custom News Sentiment Labels'].value_counts(normalize=True) * 100
texblob_sentiment_counts_news = df1['TB News Sentiment Labels'].value_counts(normalize=True) * 100

# Plot the sentiment percentages as pie charts
axes[0].pie(vader_sentiment_counts_news, labels=vader_sentiment_counts_news.index, autopct='%1.1f%%', \
            colors=['skyblue', 'lightgreen', 'lightcoral'])
axes[0].set_title('Sentiment Analysis Results for News (VADER)')

axes[1].pie(custom_sentiment_counts_news, labels=custom_sentiment_counts_news.index, autopct='%1.1f%%', \
            colors=['skyblue', 'lightgreen', 'lightcoral'])
axes[1].set_title('Sentiment Analysis Results for News (Custom Model)')

axes[2].pie(texblob_sentiment_counts_news, labels=texblob_sentiment_counts_news.index, autopct='%1.1f%%',\
             colors=['skyblue', 'lightgreen', 'lightcoral'])
axes[2].set_title('Sentiment Analysis Results for News (TextBlob)')

plt.show()


print(df.dtypes)
print(df1.dtypes)

Date                               object
Tweet                              object
Cleaned Tweet                      object
Tweet Sentiment                   float64
Tweet Sentiment Label              object
Custom Tweets Sentiment Scores    float64
Custom Tweets Sentiment Labels     object
TB Tweets Sentiment Labels         object
TB Tweets Sentiment               float64
dtype: object
News                             object
Date                             object
News Sentiment                  float64
News Sentiment Label             object
Custom News Sentiment Scores    float64
Custom News Sentiment Labels     object
TB News Sentiment Labels         object
TB Tweets Sentiment             float64
TB News Sentiment               float64
dtype: object


# Convert dates to datetime
df['Date'] = pd.to_datetime(df['Date']).dt.date
df1['Date'] = pd.to_datetime(df1['Date']).dt.date

# Aggregate daily sentiment for tweets
daily_tweets_sentiment = df.groupby('Date').agg({'TB Tweets Sentiment': 'mean'})

# Aggregate daily sentiment for news
daily_news_sentiment = df1.groupby('Date').agg({'TB News Sentiment': 'mean'})

# Combine daily sentiment
combined_sentiment = pd.merge(daily_tweets_sentiment, daily_news_sentiment, on='Date', how='outer')

# Calculate weighted average overall score
combined_sentiment['Overall Score'] = combined_sentiment[['TB Tweets Sentiment', \
                                                          'TB News Sentiment']].mean(axis=1)

combined_sentiment.head()


combined_sentiment.to_csv('combined_sentiment.csv', index=True)


df = pd.read_csv('combined_sentiment.csv')
df.head()


# @title overall_tb_polarity

from matplotlib import pyplot as plt
df['Overall Score'].plot(kind='line', figsize=(8, 4), title='overall_sentiment')
plt.gca().spines[['top', 'right']].set_visible(False)


hist = pd.read_csv('historical_prices (1).csv')
hist


# @title Adj Close

from matplotlib import pyplot as plt
hist['Adj Close'].plot(kind='line', figsize=(8, 4), title='Adj Close')
plt.gca().spines[['top', 'right']].set_visible(False)


print(df.shape)
print(hist.shape)

(1011, 4)
(662, 3)


merged_data = pd.merge(df, hist, on='Date', how='outer')
print(merged_data.shape)
merged_data

(1013, 6)


merged_data['Adj Close'].fillna(method='pad', inplace=True)
merged_data['Volume'].fillna(method='pad', inplace=True)
merged_data.head()


merged_data.isnull().sum()

Date                     0
TB Tweets Sentiment    648
TB News Sentiment       58
Overall Score            2
Adj Close                0
Volume                   0
dtype: int64


merged_data.dropna(inplace=True)
merged_data.isnull().sum()

Date                   0
TB Tweets Sentiment    0
TB News Sentiment      0
Overall Score          0
Adj Close              0
Volume                 0
dtype: int64


merged_data.to_csv('merged_data.csv', index=True)


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
import matplotlib.pyplot as plt

# Load your data
data = pd.read_csv('merged_data.csv')
# Ensure data is sorted by date
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values('Date')

# Feature Engineering: Using lagged values of Adj Close
data['Adj_Close_Lag1'] = data['Adj Close'].shift(1)
data['Adj_Close_Lag2'] = data['Adj Close'].shift(2)
data['Adj_Close_Lag3'] = data['Adj Close'].shift(3)

# Drop rows with NaN values created by shifting
data = data.dropna()

# Define features and target
features = ['overall_score', 'Volume', 'Adj_Close_Lag1', 'Adj_Close_Lag2', 'Adj_Close_Lag3']
target = 'Adj Close'

# Scale the features
scaler = MinMaxScaler()
scaled_features = scaler.fit_transform(data[features])

# Scale the target
target_scaler = MinMaxScaler()
scaled_target = target_scaler.fit_transform(data[[target]])

# Create sequences for LSTM
def create_sequences(features, target, seq_length):
    X, y = [], []
    for i in range(len(features) - seq_length):
        X.append(features[i:i + seq_length])
        y.append(target[i + seq_length])
    return np.array(X), np.array(y)

seq_length = 10  # This means we'll look back 10 days
X, y = create_sequences(scaled_features, scaled_target, seq_length)

# Split into training and testing sets
split = int(0.8 * len(X))
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

# Build the LSTM model
model = Sequential([
    LSTM(50, return_sequences=True, input_shape=(seq_length, X.shape[2])),
    Dropout(0.2),
    LSTM(50, return_sequences=False),
    Dropout(0.2),
    Dense(25),
    Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

# Predict on the test set
y_pred = model.predict(X_test)

# Inverse transform the predictions and the actual values
y_pred = target_scaler.inverse_transform(y_pred)
y_test = target_scaler.inverse_transform(y_test)

# Calculate RMSE
rmse_lstm = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'LSTM RMSE: {rmse_lstm}')

# Plot the results
plt.figure(figsize=(14, 5))
plt.plot(data['Date'].iloc[split + seq_length:], y_test, color='blue', label='Actual Adj Close')
plt.plot(data['Date'].iloc[split + seq_length:], y_pred, color='red', label='Predicted Adj Close')
plt.xlabel('Date')
plt.ylabel('Adj Close')
plt.title('Actual vs Predicted Adj Close')
plt.legend()
plt.show()

Epoch 1/50
25/25 [==============================] - 9s 53ms/step - loss: 0.0679 - val_loss: 0.0031
Epoch 2/50
25/25 [==============================] - 0s 12ms/step - loss: 0.0115 - val_loss: 0.0026
Epoch 3/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0082 - val_loss: 0.0026
Epoch 4/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0075 - val_loss: 0.0030
Epoch 5/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0072 - val_loss: 0.0026
Epoch 6/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0064 - val_loss: 0.0020
Epoch 7/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0069 - val_loss: 0.0027
Epoch 8/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0063 - val_loss: 0.0022
Epoch 9/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0069 - val_loss: 0.0027
Epoch 10/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0062 - val_loss: 0.0019
Epoch 11/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0061 - val_loss: 0.0027
Epoch 12/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0053 - val_loss: 0.0020
Epoch 13/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0055 - val_loss: 0.0019
Epoch 14/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0057 - val_loss: 0.0017
Epoch 15/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0055 - val_loss: 0.0018
Epoch 16/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0051 - val_loss: 0.0020
Epoch 17/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0050 - val_loss: 0.0017
Epoch 18/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0058 - val_loss: 0.0019
Epoch 19/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0047 - val_loss: 0.0017
Epoch 20/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0052 - val_loss: 0.0025
Epoch 21/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0046 - val_loss: 0.0016
Epoch 22/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0043 - val_loss: 0.0018
Epoch 23/50
25/25 [==============================] - 0s 12ms/step - loss: 0.0046 - val_loss: 0.0017
Epoch 24/50
25/25 [==============================] - 0s 12ms/step - loss: 0.0044 - val_loss: 0.0022
Epoch 25/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0047 - val_loss: 0.0025
Epoch 26/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0047 - val_loss: 0.0030
Epoch 27/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0042 - val_loss: 0.0020
Epoch 28/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0041 - val_loss: 0.0015
Epoch 29/50
25/25 [==============================] - 0s 20ms/step - loss: 0.0044 - val_loss: 0.0021
Epoch 30/50
25/25 [==============================] - 1s 21ms/step - loss: 0.0043 - val_loss: 0.0015
Epoch 31/50
25/25 [==============================] - 1s 20ms/step - loss: 0.0045 - val_loss: 0.0015
Epoch 32/50
25/25 [==============================] - 1s 21ms/step - loss: 0.0042 - val_loss: 0.0016
Epoch 33/50
25/25 [==============================] - 1s 21ms/step - loss: 0.0038 - val_loss: 0.0015
Epoch 34/50
25/25 [==============================] - 1s 22ms/step - loss: 0.0035 - val_loss: 0.0014
Epoch 35/50
25/25 [==============================] - 0s 20ms/step - loss: 0.0036 - val_loss: 0.0013
Epoch 36/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0033 - val_loss: 0.0021
Epoch 37/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0035 - val_loss: 0.0018
Epoch 38/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0036 - val_loss: 0.0014
Epoch 39/50
25/25 [==============================] - 0s 15ms/step - loss: 0.0034 - val_loss: 0.0017
Epoch 40/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0033 - val_loss: 0.0015
Epoch 41/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0034 - val_loss: 0.0013
Epoch 42/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0035 - val_loss: 0.0017
Epoch 43/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0033 - val_loss: 0.0014
Epoch 44/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0031 - val_loss: 0.0014
Epoch 45/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0033 - val_loss: 0.0014
Epoch 46/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0032 - val_loss: 0.0013
Epoch 47/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0031 - val_loss: 0.0012
Epoch 48/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0032 - val_loss: 0.0021
Epoch 49/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0031 - val_loss: 0.0012
Epoch 50/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0031 - val_loss: 0.0014
7/7 [==============================] - 1s 5ms/step
LSTM RMSE: 11.247246177120926


import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Convert predictions and actual values to binary classes
def to_directional_classes(values):
    return np.where(np.diff(values, axis=0) > 0, 1, 0)

# Apply the function to y_test and y_pred
y_test_classes = to_directional_classes(y_test)
y_pred_classes = to_directional_classes(y_pred)

# Compute classification metrics
accuracy = accuracy_score(y_test_classes, y_pred_classes)
precision = precision_score(y_test_classes, y_pred_classes)
recall = recall_score(y_test_classes, y_pred_classes)
f1 = f1_score(y_test_classes, y_pred_classes)

print(f'LSTM Classifier Accuracy: {accuracy}')
print(f'LSTM Classifier Precision: {precision}')
print(f'LSTM Classifier Recall: {recall}')
print(f'LSTM Classifier F1 Score: {f1}')

LSTM Classifier Accuracy: 0.45226130653266333
LSTM Classifier Precision: 0.3176470588235294
LSTM Classifier Recall: 0.34615384615384615
LSTM Classifier F1 Score: 0.3312883435582822


import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
import matplotlib.pyplot as plt

# Load your data
data = pd.read_csv('merged_data.csv')
# Ensure data is sorted by date
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values('Date')

# Feature Engineering: Using lagged values of Adj Close
data['Adj_Close_Lag1'] = data['Adj Close'].shift(1)
data['Adj_Close_Lag2'] = data['Adj Close'].shift(2)
data['Adj_Close_Lag3'] = data['Adj Close'].shift(3)
data['Adj_Close_Lag4'] = data['Adj Close'].shift(4)
data['Adj_Close_Lag5'] = data['Adj Close'].shift(5)
data['Adj_Close_Lag6'] = data['Adj Close'].shift(6)
data['Adj_Close_Lag7'] = data['Adj Close'].shift(7)
data['Adj_Close_Lag8'] = data['Adj Close'].shift(8)
data['Adj_Close_Lag9'] = data['Adj Close'].shift(9)
data['Adj_Close_Lag10'] = data['Adj Close'].shift(10)
data['Adj_Close_Lag11'] = data['Adj Close'].shift(11)
data['Adj_Close_Lag12'] = data['Adj Close'].shift(12)

# Drop rows with NaN values created by shifting
data = data.dropna()

# Define features and target
features = ['overall_score', 'Volume', 'Adj_Close_Lag1', 'Adj_Close_Lag2', 'Adj_Close_Lag3', 'Adj_Close_Lag4',
            'Adj_Close_Lag5', 'Adj_Close_Lag6', 'Adj_Close_Lag7', 'Adj_Close_Lag8', 'Adj_Close_Lag9', 
            'Adj_Close_Lag10',
            'Adj_Close_Lag11', 'Adj_Close_Lag12']
target = 'Adj Close'

# Scale the features
scaler = MinMaxScaler()
scaled_features = scaler.fit_transform(data[features])

# Scale the target
target_scaler = MinMaxScaler()
scaled_target = target_scaler.fit_transform(data[[target]])

# Create sequences for LSTM
def create_sequences(features, target, seq_length):
    X, y = [], []
    for i in range(len(features) - seq_length):
        X.append(features[i:i + seq_length])
        y.append(target[i + seq_length])
    return np.array(X), np.array(y)

seq_length = 10  # This means we'll look back 10 days
X, y = create_sequences(scaled_features, scaled_target, seq_length)

# Split into training and testing sets
split = int(0.8 * len(X))
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

# Build the LSTM model
model = Sequential([
    LSTM(50, return_sequences=True, input_shape=(seq_length, X.shape[2])),
    Dropout(0.2),
    LSTM(50, return_sequences=False),
    Dropout(0.2),
    Dense(25),
    Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

# Predict on the test set
y_pred = model.predict(X_test)

# Inverse transform the predictions and the actual values
y_pred = target_scaler.inverse_transform(y_pred)
y_test = target_scaler.inverse_transform(y_test)

# Calculate RMSE
rmse_lstm = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'LSTM RMSE: {rmse_lstm}')

# Plot the results
plt.figure(figsize=(14, 5))
plt.plot(data['Date'].iloc[split + seq_length:], y_test, color='blue', label='Actual Adj Close')
plt.plot(data['Date'].iloc[split + seq_length:], y_pred, color='red', label='Predicted Adj Close')
plt.xlabel('Date')
plt.ylabel('Adj Close')
plt.title('Actual vs Predicted Adj Close')
plt.legend()
plt.show()

Epoch 1/50
25/25 [==============================] - 9s 49ms/step - loss: 0.0404 - val_loss: 0.0097
Epoch 2/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0106 - val_loss: 0.0055
Epoch 3/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0080 - val_loss: 0.0031
Epoch 4/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0076 - val_loss: 0.0024
Epoch 5/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0067 - val_loss: 0.0025
Epoch 6/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0066 - val_loss: 0.0022
Epoch 7/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0064 - val_loss: 0.0024
Epoch 8/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0062 - val_loss: 0.0028
Epoch 9/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0062 - val_loss: 0.0025
Epoch 10/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0057 - val_loss: 0.0022
Epoch 11/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0054 - val_loss: 0.0024
Epoch 12/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0059 - val_loss: 0.0020
Epoch 13/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0049 - val_loss: 0.0020
Epoch 14/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0055 - val_loss: 0.0054
Epoch 15/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0055 - val_loss: 0.0022
Epoch 16/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0048 - val_loss: 0.0021
Epoch 17/50
25/25 [==============================] - 0s 15ms/step - loss: 0.0052 - val_loss: 0.0022
Epoch 18/50
25/25 [==============================] - 1s 21ms/step - loss: 0.0050 - val_loss: 0.0024
Epoch 19/50
25/25 [==============================] - 1s 20ms/step - loss: 0.0048 - val_loss: 0.0024
Epoch 20/50
25/25 [==============================] - 1s 20ms/step - loss: 0.0048 - val_loss: 0.0019
Epoch 21/50
25/25 [==============================] - 1s 22ms/step - loss: 0.0046 - val_loss: 0.0019
Epoch 22/50
25/25 [==============================] - 1s 23ms/step - loss: 0.0042 - val_loss: 0.0029
Epoch 23/50
25/25 [==============================] - 1s 23ms/step - loss: 0.0041 - val_loss: 0.0017
Epoch 24/50
25/25 [==============================] - 0s 16ms/step - loss: 0.0043 - val_loss: 0.0017
Epoch 25/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0043 - val_loss: 0.0017
Epoch 26/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0041 - val_loss: 0.0023
Epoch 27/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0044 - val_loss: 0.0017
Epoch 28/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0043 - val_loss: 0.0019
Epoch 29/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0038 - val_loss: 0.0016
Epoch 30/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0042 - val_loss: 0.0017
Epoch 31/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0037 - val_loss: 0.0023
Epoch 32/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0040 - val_loss: 0.0016
Epoch 33/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0034 - val_loss: 0.0018
Epoch 34/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0044 - val_loss: 0.0025
Epoch 35/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0032 - val_loss: 0.0018
Epoch 36/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0033 - val_loss: 0.0015
Epoch 37/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0033 - val_loss: 0.0021
Epoch 38/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0032 - val_loss: 0.0019
Epoch 39/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0029 - val_loss: 0.0019
Epoch 40/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0032 - val_loss: 0.0014
Epoch 41/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0032 - val_loss: 0.0019
Epoch 42/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0032 - val_loss: 0.0030
Epoch 43/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0032 - val_loss: 0.0014
Epoch 44/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0029 - val_loss: 0.0014
Epoch 45/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0029 - val_loss: 0.0017
Epoch 46/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0030 - val_loss: 0.0014
Epoch 47/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0029 - val_loss: 0.0014
Epoch 48/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0028 - val_loss: 0.0024
Epoch 49/50
25/25 [==============================] - 0s 14ms/step - loss: 0.0030 - val_loss: 0.0015
Epoch 50/50
25/25 [==============================] - 0s 13ms/step - loss: 0.0028 - val_loss: 0.0015
7/7 [==============================] - 1s 6ms/step
LSTM RMSE: 11.876066953850351


import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Convert predictions and actual values to binary classes
def to_directional_classes(values):
    return np.where(np.diff(values, axis=0) > 0, 1, 0)

# Apply the function to y_test and y_pred
y_test_classes = to_directional_classes(y_test)
y_pred_classes = to_directional_classes(y_pred)

# Compute classification metrics
accuracy = accuracy_score(y_test_classes, y_pred_classes)
precision = precision_score(y_test_classes, y_pred_classes)
recall = recall_score(y_test_classes, y_pred_classes)
f1 = f1_score(y_test_classes, y_pred_classes)

print(f'LSTM Classifier Accuracy: {accuracy}')
print(f'LSTM Classifier Precision: {precision}')
print(f'LSTM Classifier Recall: {recall}')
print(f'LSTM Classifier F1 Score: {f1}')

LSTM Classifier Accuracy: 0.4010152284263959
LSTM Classifier Precision: 0.26436781609195403
LSTM Classifier Recall: 0.2987012987012987
LSTM Classifier F1 Score: 0.28048780487804875


import pandas as pd
import numpy as np
from sklearn.model_selection import TimeSeriesSplit, train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_squared_error, accuracy_score, precision_score, recall_score, f1_score
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt

# Load your data
data = pd.read_csv('merged_data.csv')

# Ensure data is sorted by date
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values('Date')

# Feature Engineering: Using lagged values of Adj Close
data['Adj_Close_Lag1'] = data['Adj Close'].shift(1)
data['Adj_Close_Lag2'] = data['Adj Close'].shift(2)
data['Adj_Close_Lag3'] = data['Adj Close'].shift(3)

# Drop rows with NaN values created by shifting
data = data.dropna()

# Define features and target
features = ['overall_score', 'Volume', 'Adj_Close_Lag1', 'Adj_Close_Lag2', 'Adj_Close_Lag3']
target = 'Adj Close'

# Scale the features and target
scaler = MinMaxScaler()
scaled_features = scaler.fit_transform(data[features])
scaled_target = scaler.fit_transform(data[[target]])

# Create sequences
def create_sequences(features, target, seq_length):
    X, y = [], []
    for i in range(len(features) - seq_length):
        X.append(features[i:i + seq_length])
        y.append(target[i + seq_length])
    return np.array(X), np.array(y)

seq_length = 10  # This means we'll look back 10 days
X, y = create_sequences(scaled_features, scaled_target, seq_length)

# Split into training and testing sets
split = int(0.8 * len(X))
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

# Reshape for the models
X_train_flat = X_train.reshape(X_train.shape[0], -1)
X_test_flat = X_test.reshape(X_test.shape[0], -1)

# Linear Regression
lr_model = LinearRegression()
lr_model.fit(X_train_flat, y_train)
y_pred_lr = lr_model.predict(X_test_flat)
rmse_lr = np.sqrt(mean_squared_error(y_test, y_pred_lr))
print(f'Linear Regression RMSE: {rmse_lr}')

# Random Forest Regressor
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train_flat, y_train)
y_pred_rf = rf_model.predict(X_test_flat)
rmse_rf = np.sqrt(mean_squared_error(y_test, y_pred_rf))
print(f'Random Forest RMSE: {rmse_rf}')

# Convert to classification problem (up/down)
def get_direction(y):
    return np.where(np.diff(y, prepend=y[0]) > 0, 1, 0)

y_train_direction = get_direction(y_train.flatten())
y_test_direction = get_direction(y_test.flatten())
y_pred_lr_direction = get_direction(y_pred_lr.flatten())
y_pred_rf_direction = get_direction(y_pred_rf.flatten())

# Accuracy metrics for Linear Regression
accuracy_lr = accuracy_score(y_test_direction, y_pred_lr_direction)
precision_lr = precision_score(y_test_direction, y_pred_lr_direction)
recall_lr = recall_score(y_test_direction, y_pred_lr_direction)
f1_lr = f1_score(y_test_direction, y_pred_lr_direction)

print(f'Linear Regression Accuracy: {accuracy_lr}')
print(f'Linear Regression Precision: {precision_lr}')
print(f'Linear Regression Recall: {recall_lr}')
print(f'Linear Regression F1 Score: {f1_lr}')

# Accuracy metrics for Random Forest
accuracy_rf = accuracy_score(y_test_direction, y_pred_rf_direction)
precision_rf = precision_score(y_test_direction, y_pred_rf_direction)
recall_rf = recall_score(y_test_direction, y_pred_rf_direction)
f1_rf = f1_score(y_test_direction, y_pred_rf_direction)

print(f'Random Forest Accuracy: {accuracy_rf}')
print(f'Random Forest Precision: {precision_rf}')
print(f'Random Forest Recall: {recall_rf}')
print(f'Random Forest F1 Score: {f1_rf}')

# Inverse transform the predictions and the actual values
y_test_inv = scaler.inverse_transform(np.hstack((np.zeros((y_test.shape[0], scaled_features.shape[1] - 1))\
                                                 , y_test.reshape(-1, 1))))[:, -1]
y_pred_lr_inv = scaler.inverse_transform(np.hstack((np.zeros((y_pred_lr.shape[0], \
                                                              scaled_features.shape[1] - 1)), \
                                                                y_pred_lr.reshape(-1, 1))))[:, -1]
y_pred_rf_inv = scaler.inverse_transform(np.hstack((np.zeros((y_pred_rf.shape[0],\
                                                               scaled_features.shape[1] - 1)), \
                                                                y_pred_rf.reshape(-1, 1))))[:, -1]

# Plot the results for Linear Regression
plt.figure(figsize=(14, 5))
plt.plot(data['Date'].iloc[split + seq_length:], y_test_inv, color='blue', label='Actual Adj Close')
plt.plot(data['Date'].iloc[split + seq_length:], y_pred_lr_inv, color='red', \
         label='Predicted Adj Close (Linear Regression)')
plt.xlabel('Date')
plt.ylabel('Adj Close')
plt.title('Linear Regression: Actual vs Predicted Adj Close')
plt.legend()
plt.show()

# Plot the results for Random Forest
plt.figure(figsize=(14, 5))
plt.plot(data['Date'].iloc[split + seq_length:], y_test_inv, color='blue', label='Actual Adj Close')
plt.plot(data['Date'].iloc[split + seq_length:], y_pred_rf_inv, color='green', \
         label='Predicted Adj Close (Random Forest)')
plt.xlabel('Date')
plt.ylabel('Adj Close')
plt.title('Random Forest: Actual vs Predicted Adj Close')
plt.legend()
plt.show()

Linear Regression RMSE: 0.030402549913238038

<ipython-input-19-e78d3c537186>:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  rf_model.fit(X_train_flat, y_train)

Random Forest RMSE: 0.03219945357102593
Linear Regression Accuracy: 0.475
Linear Regression Precision: 0.3548387096774194
Linear Regression Recall: 0.4230769230769231
Linear Regression F1 Score: 0.3859649122807018
Random Forest Accuracy: 0.51
Random Forest Precision: 0.38636363636363635
Random Forest Recall: 0.4358974358974359
Random Forest F1 Score: 0.40963855421686746


import pandas as pd
import numpy as np
from sklearn.model_selection import TimeSeriesSplit, train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_squared_error, accuracy_score, precision_score, recall_score, f1_score
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt

# Load your data
data = pd.read_csv('merged_data.csv')

# Ensure data is sorted by date
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values('Date')

# Feature Engineering: Using lagged values of Adj Close
data['Adj_Close_Lag1'] = data['Adj Close'].shift(1)
data['Adj_Close_Lag2'] = data['Adj Close'].shift(2)
data['Adj_Close_Lag3'] = data['Adj Close'].shift(3)
data['Adj_Close_Lag4'] = data['Adj Close'].shift(4)
data['Adj_Close_Lag5'] = data['Adj Close'].shift(5)
data['Adj_Close_Lag6'] = data['Adj Close'].shift(6)
data['Adj_Close_Lag7'] = data['Adj Close'].shift(7)
data['Adj_Close_Lag8'] = data['Adj Close'].shift(8)
data['Adj_Close_Lag9'] = data['Adj Close'].shift(9)
data['Adj_Close_Lag10'] = data['Adj Close'].shift(10)
data['Adj_Close_Lag11'] = data['Adj Close'].shift(11)
data['Adj_Close_Lag12'] = data['Adj Close'].shift(12)

# Drop rows with NaN values created by shifting
data = data.dropna()

# Define features and target
features = ['overall_score', 'Volume', 'Adj_Close_Lag1', 'Adj_Close_Lag2', 'Adj_Close_Lag3', 'Adj_Close_Lag4',
            'Adj_Close_Lag5', 'Adj_Close_Lag6', 'Adj_Close_Lag7', 'Adj_Close_Lag8', 'Adj_Close_Lag9', 
            'Adj_Close_Lag10',
            'Adj_Close_Lag11', 'Adj_Close_Lag12']
target = 'Adj Close'

# Scale the features and target
scaler = MinMaxScaler()
scaled_features = scaler.fit_transform(data[features])
scaled_target = scaler.fit_transform(data[[target]])

# Create sequences for LSTM
def create_sequences(features, target, seq_length):
    X, y = [], []
    for i in range(len(features) - seq_length):
        X.append(features[i:i + seq_length])
        y.append(target[i + seq_length])
    return np.array(X), np.array(y)

seq_length = 10  # This means we'll look back 10 days
X, y = create_sequences(scaled_features, scaled_target, seq_length)

# Split into training and testing sets
split = int(0.8 * len(X))
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

# Reshape for the models
X_train_flat = X_train.reshape(X_train.shape[0], -1)
X_test_flat = X_test.reshape(X_test.shape[0], -1)

# Linear Regression
lr_model = LinearRegression()
lr_model.fit(X_train_flat, y_train)
y_pred_lr = lr_model.predict(X_test_flat)
rmse_lr = np.sqrt(mean_squared_error(y_test, y_pred_lr))
print(f'Linear Regression RMSE: {rmse_lr}')

# Random Forest Regressor
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train_flat, y_train)
y_pred_rf = rf_model.predict(X_test_flat)
rmse_rf = np.sqrt(mean_squared_error(y_test, y_pred_rf))
print(f'Random Forest RMSE: {rmse_rf}')

# Convert to classification problem (up/down)
def get_direction(y):
    return np.where(np.diff(y, prepend=y[0]) > 0, 1, 0)

y_train_direction = get_direction(y_train.flatten())
y_test_direction = get_direction(y_test.flatten())
y_pred_lr_direction = get_direction(y_pred_lr.flatten())
y_pred_rf_direction = get_direction(y_pred_rf.flatten())

# Accuracy metrics for Linear Regression
accuracy_lr = accuracy_score(y_test_direction, y_pred_lr_direction)
precision_lr = precision_score(y_test_direction, y_pred_lr_direction)
recall_lr = recall_score(y_test_direction, y_pred_lr_direction)
f1_lr = f1_score(y_test_direction, y_pred_lr_direction)

print(f'Linear Regression Accuracy: {accuracy_lr}')
print(f'Linear Regression Precision: {precision_lr}')
print(f'Linear Regression Recall: {recall_lr}')
print(f'Linear Regression F1 Score: {f1_lr}')

# Accuracy metrics for Random Forest
accuracy_rf = accuracy_score(y_test_direction, y_pred_rf_direction)
precision_rf = precision_score(y_test_direction, y_pred_rf_direction)
recall_rf = recall_score(y_test_direction, y_pred_rf_direction)
f1_rf = f1_score(y_test_direction, y_pred_rf_direction)

print(f'Random Forest Accuracy: {accuracy_rf}')
print(f'Random Forest Precision: {precision_rf}')
print(f'Random Forest Recall: {recall_rf}')
print(f'Random Forest F1 Score: {f1_rf}')

# Inverse transform the predictions and the actual values
y_test_inv = scaler.inverse_transform(np.hstack((np.zeros((y_test.shape[0], \
                                                           scaled_features.shape[1] - 1)), \
                                                            y_test.reshape(-1, 1))))[:, -1]
y_pred_lr_inv = scaler.inverse_transform(np.hstack((np.zeros((y_pred_lr.shape[0], \
                                                              scaled_features.shape[1] - 1)),\
                                                                  y_pred_lr.reshape(-1, 1))))[:, -1]
y_pred_rf_inv = scaler.inverse_transform(np.hstack((np.zeros((y_pred_rf.shape[0], \
                                                              scaled_features.shape[1] - 1)),\
                                                                  y_pred_rf.reshape(-1, 1))))[:, -1]

# Plot the results for Linear Regression
plt.figure(figsize=(14, 5))
plt.plot(data['Date'].iloc[split + seq_length:], y_test_inv, color='blue', label='Actual Adj Close')
plt.plot(data['Date'].iloc[split + seq_length:], y_pred_lr_inv, color='red', \
         label='Predicted Adj Close (Linear Regression)')
plt.xlabel('Date')
plt.ylabel('Adj Close')
plt.title('Linear Regression: Actual vs Predicted Adj Close')
plt.legend()
plt.show()

# Plot the results for Random Forest
plt.figure(figsize=(14, 5))
plt.plot(data['Date'].iloc[split + seq_length:], y_test_inv, color='blue', label='Actual Adj Close')
plt.plot(data['Date'].iloc[split + seq_length:], y_pred_rf_inv, color='green',\
          label='Predicted Adj Close (Random Forest)')
plt.xlabel('Date')
plt.ylabel('Adj Close')
plt.title('Random Forest: Actual vs Predicted Adj Close')
plt.legend()
plt.show()

Linear Regression RMSE: 0.03108188676502338

<ipython-input-26-06d5c9379b85>:76: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  rf_model.fit(X_train_flat, y_train)

Random Forest RMSE: 0.03210547275620806
Linear Regression Accuracy: 0.48484848484848486
Linear Regression Precision: 0.37373737373737376
Linear Regression Recall: 0.4805194805194805
Linear Regression F1 Score: 0.42045454545454547
Random Forest Accuracy: 0.5656565656565656
Random Forest Precision: 0.449438202247191
Random Forest Recall: 0.5194805194805194
Random Forest F1 Score: 0.48192771084337344


from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

# Inverse transform the predictions and the actual values
y_test_inv = scaler.inverse_transform(np.hstack((np.zeros((y_test.shape[0], \
                                                           scaled_features.shape[1] - 1)), \
                                                            y_test.reshape(-1, 1))))[:, -1]
y_pred_rf_inv = scaler.inverse_transform(np.hstack((np.zeros((y_pred_rf.shape[0], \
                                                              scaled_features.shape[1] - 1)), \
                                                    y_pred_rf.reshape(-1, 1))))[:, -1]

# Calculate hypothetical trading profits or losses
def calculate_profits(y_test_inv, y_pred_rf_inv):
    """
    Calculate hypothetical profits based on actual and predicted values.
    Buy (long) if predicted price will go up, Sell (short) if predicted price will go down.
    """
    profits = []
    for i in range(1, len(y_test_inv)):
        if y_pred_rf_direction[i-1] == 1:  # predicted price will go up
            profits.append(y_test_inv[i] - y_test_inv[i-1])
        elif y_pred_rf_direction[i-1] == 0:  # predicted price will go down
            profits.append(y_test_inv[i-1] - y_test_inv[i])
        else:
            profits.append(0)  # no action taken

    return np.array(profits)

# Calculate profits
profits = calculate_profits(y_test_inv, y_pred_rf_inv)

# Analyze the variability of profitability
mean_profit = np.mean(profits)
std_dev_profit = np.std(profits)
max_profit = np.max(profits)
min_profit = np.min(profits)

# Confusion Matrix to understand the model predictions
cm = confusion_matrix(y_test_direction, y_pred_rf_direction)

# Output results
print("Confusion Matrix:")
print(cm)
print("\nMean Profit: ", mean_profit)
print("Standard Deviation of Profit: ", std_dev_profit)
print("Maximum Profit: ", max_profit)
print("Minimum Profit: ", min_profit)

# Visualize profits
plt.figure(figsize=(10, 6))
plt.plot(profits, label='Daily Profits')
plt.axhline(y=mean_profit, color='r', linestyle='-', label=f'Mean Profit: {mean_profit:.2f}')
plt.axhline(y=0, color='k', linestyle='--')
plt.title('Hypothetical Daily Profits Based on Model Predictions')
plt.xlabel('Days')
plt.ylabel('Profit')
plt.legend()
plt.show()

Confusion Matrix:
[[72 49]
 [37 40]]

Mean Profit:  -90.76019783224571
Standard Deviation of Profit:  1709.5243199146155
Maximum Profit:  7776.174218701199
Minimum Profit:  -3879.0267715911905


# Inverse transform the predictions and the actual values
y_test_inv = scaler.inverse_transform(np.hstack((np.zeros((y_test.shape[0], \
                                                           scaled_features.shape[1] - 1)), \
                                                            y_test.reshape(-1, 1))))[:, -1]
y_pred_rf_inv = scaler.inverse_transform(np.hstack((np.zeros((y_pred_rf.shape[0], \
                                                              scaled_features.shape[1] - 1)), \
                                                                y_pred_rf.reshape(-1, 1))))[:, -1]

# Hypothetical trading strategy
# Buy if tomorrow's predicted price is higher than today's price, sell otherwise
test_dates = data['Date'].iloc[split + seq_length:]
test = pd.DataFrame({'Date': test_dates, 'Adj Close': y_test_inv, \
                     'Predicted Adj Close': y_pred_rf_inv})
test['Price Change'] = test['Predicted Adj Close'].diff()
test['Signal'] = np.where(test['Price Change'] > 0, 1, -1)

# Calculate daily returns assuming we invest 1 unit of currency per trade
test['Daily Return'] = test['Signal'] * test['Adj Close'].pct_change()
test['Cumulative Return'] = (1 + test['Daily Return']).cumprod()

# Calculate rolling standard deviation of daily returns (variability)
rolling_window = 20
test['Rolling Std'] = test['Daily Return'].rolling(window=rolling_window).std()

# Plot cumulative returns and rolling standard deviation
fig, ax1 = plt.subplots(figsize=(14, 7))

color = 'tab:blue'
ax1.set_xlabel('Date')
ax1.set_ylabel('Cumulative Return', color=color)
ax1.plot(test['Date'], test['Cumulative Return'], color=color, label='Cumulative Return (Random Forest)')
ax1.tick_params(axis='y', labelcolor=color)
ax1.legend(loc='upper left')

ax2 = ax1.twinx()
color = 'tab:red'
ax2.set_ylabel('Rolling Std Dev', color=color)
ax2.plot(test['Date'], test['Rolling Std'], color=color, linestyle='--', label='Rolling Std Dev (20 days)')
ax2.tick_params(axis='y', labelcolor=color)
ax2.legend(loc='upper right')

plt.title('Random Forest: Cumulative Return and Rolling Standard Deviation Over Time')
fig.tight_layout()
plt.show()

# Analyze variability of profitability
mean_return = test['Daily Return'].mean()
std_return = test['Daily Return'].std()
print(f'Mean Daily Return: {mean_return}')
print(f'Standard Deviation of Daily Return: {std_return}')

Mean Daily Return: 0.002287662371983815
Standard Deviation of Daily Return: 0.02877100685645788


# Create a DataFrame with 'Date' and 'Adj Close' columns
df = pd.DataFrame({'Date': test['Date'], 'Adj Close': y_test_inv})

# Calculate 'Daily Return' and 'Cumulative Return'
df['Daily Return'] = df['Adj Close'].pct_change()
df['Cumulative Return'] = df['Daily Return'].cumsum()

# Determine 'Action'
df['Trading Signal'] = np.where(df['Daily Return'] > 0, 'Buy', 'Sell')

# Display the DataFrame
df


# Define stop-loss threshold
stop_loss_threshold = -0.01  # Adjust this threshold as needed

# Plot cumulative returns and rolling standard deviation
fig, ax1 = plt.subplots(figsize=(14, 7))

color = 'tab:blue'
ax1.set_xlabel('Date')
ax1.set_ylabel('Cumulative Return', color=color)
ax1.plot(test['Date'], test['Cumulative Return'], color=color, label='Cumulative Return (Random Forest)')
ax1.tick_params(axis='y', labelcolor=color)
ax1.legend(loc='upper left')

ax2 = ax1.twinx()
color = 'tab:red'
ax2.set_ylabel('Rolling Std Dev', color=color)
ax2.plot(test['Date'], test['Rolling Std'], color=color, linestyle='--', label='Rolling Std Dev (20 days)')
ax2.tick_params(axis='y', labelcolor=color)
ax2.legend(loc='upper right')

# Add stop-loss level
ax1.axhline(y=(1 + stop_loss_threshold), color='gray', linestyle='--', \
            label=f'Stop Loss: {stop_loss_threshold*100}%')
ax1.legend(loc='upper left')

plt.title('Random Forest: Cumulative Return and Rolling Standard Deviation Over Time with Stop Loss')
fig.tight_layout()
plt.show()


# Define stop-loss threshold
stop_loss_threshold = -0.01

# Calculate 'Action' with stop-loss consideration
df['Trading Signal'] = np.where((df['Daily Return'] > 0) & (df['Cumulative Return'] > stop_loss_threshold), 'Buy',
                                np.where((df['Daily Return'] <= 0) & (df['Cumulative Return'] > stop_loss_threshold), 'Sell', 'Stop Loss'))

# Display the updated DataFrame
df

	Date	Tweet
0	2022-09-29	Mainstream media has done an amazing job at br...
1	2022-09-29	Tesla delivery estimates are at around 364k fr...
2	2022-09-29	3/ Even if I include 63.0M unvested RSUs as of...
3	2022-09-29	@RealDanODowd @WholeMarsBlog @Tesla Hahaha why...
4	2022-09-29	@RealDanODowd @Tesla Stop trying to kill kids,...

	Date	Tweet	Cleaned Tweet
0	2022-09-29	Mainstream media has done an amazing job at br...	Mainstream media has done an amazing job at br...
1	2022-09-29	Tesla delivery estimates are at around 364k fr...	Tesla delivery estimates are at around 364k fr...
2	2022-09-29	3/ Even if I include 63.0M unvested RSUs as of...	3/ Even if I include 63.0M unvested RSUs as of...
3	2022-09-29	@RealDanODowd @WholeMarsBlog @Tesla Hahaha why...	Hahaha why are you still trying to stop Tesla ...
4	2022-09-29	@RealDanODowd @Tesla Stop trying to kill kids,...	Stop trying to kill kids, you sad deranged old...
...	...	...	...
37417	2021-09-30	Playing in the dirt and #chasingsunsets\n@tesl...	Playing in the dirt and
37418	2021-09-30	I agree with @freshjiva that $TSLA ‘s EV busin...	I agree with that $TSLA ‘s EV business alone i...
37419	2021-09-30	Hold. On. Tight. $TSLA	Hold. On. Tight. $TSLA
37420	2021-09-30	Get ready for a $TSLA _ _ _ _ _ _ Q3 delivery...	Get ready for a $TSLA _ _ _ _ _ _ Q3 delivery ...
37421	2021-09-30	In other words, AMD has been giving Tesla pref...	In other words, AMD has been giving Tesla pref...

	Date	Tweet	Cleaned Tweet
0	2022-09-29	Mainstream media has done an amazing job at br...	mainstream medium do amazing job brainwash peo...
1	2022-09-29	Tesla delivery estimates are at around 364k fr...	tesla delivery estimate around k analyst tsla
2	2022-09-29	3/ Even if I include 63.0M unvested RSUs as of...	even include unvested rsus additional equity n...
3	2022-09-29	@RealDanODowd @WholeMarsBlog @Tesla Hahaha why...	hahaha still try stop tesla fsd bro get shit t...
4	2022-09-29	@RealDanODowd @Tesla Stop trying to kill kids,...	stop try kill kid sad derange old man
...	...	...	...
37417	2021-09-30	Playing in the dirt and #chasingsunsets\n@tesl...	play dirt
37418	2021-09-30	I agree with @freshjiva that $TSLA ‘s EV busin...	agree tsla ev business alone worth gt sh wo fs...
37419	2021-09-30	Hold. On. Tight. $TSLA	hold tight tsla
37420	2021-09-30	Get ready for a $TSLA _ _ _ _ _ _ Q3 delivery...	get ready tsla _ _ _ _ _ _ q delivery number u...
37421	2021-09-30	In other words, AMD has been giving Tesla pref...	word amd give tesla preferential treatment bc ...

	Date	Tweet	Cleaned Tweet
0	2022-09-29	Mainstream media has done an amazing job at br...	mainstream medium do amazing job brainwash peo...
1	2022-09-29	Tesla delivery estimates are at around 364k fr...	delivery estimate around k analyst
2	2022-09-29	3/ Even if I include 63.0M unvested RSUs as of...	even include unvested rsus additional equity n...
3	2022-09-29	@RealDanODowd @WholeMarsBlog @Tesla Hahaha why...	hahaha still try stop fsd bro get shit togethe...
4	2022-09-29	@RealDanODowd @Tesla Stop trying to kill kids,...	stop try kill kid sad derange old man
...	...	...	...
37417	2021-09-30	Playing in the dirt and #chasingsunsets\n@tesl...	play dirt
37418	2021-09-30	I agree with @freshjiva that $TSLA ‘s EV busin...	agree ev business alone worth gt sh wo fsd lik...
37419	2021-09-30	Hold. On. Tight. $TSLA	hold tight
37420	2021-09-30	Get ready for a $TSLA _ _ _ _ _ _ Q3 delivery...	get ready _ _ _ _ _ _ q delivery number ur answer
37421	2021-09-30	In other words, AMD has been giving Tesla pref...	word amd give preferential treatment bc know l...

	Open	High	Low	Close	Adj Close	Volume
Date
2021-09-30	260.333344	263.043335	258.333344	258.493347	258.493347	53868000
2021-10-01	259.466675	260.260010	254.529999	258.406677	258.406677	51094200
2021-10-04	265.500000	268.989990	258.706665	260.510010	260.510010	91449900
2021-10-05	261.600006	265.769989	258.066681	260.196655	260.196655	55297800
2021-10-06	258.733337	262.220001	257.739990	260.916656	260.916656	43898400
...	...	...	...	...	...	...
2024-05-13	170.000000	175.399994	169.000000	171.889999	171.889999	67018900
2024-05-14	174.500000	179.490005	174.070007	177.550003	177.550003	86407400
2024-05-15	179.899994	180.000000	173.110001	173.990005	173.990005	79663000
2024-05-16	174.100006	175.789993	171.429993	174.839996	174.839996	59812200
2024-05-17	173.550003	179.630005	172.750000	177.460007	177.460007	76627600

Before Preprocessing...¶

Removing mentions and links from the tweets¶

Lowecasing & Removing punctuations and stopwords & Lemmatization¶

Historical Prices of Tesla¶

Before Preprocessing...¶

Lowecasing & Removing punctuations and stopwords & Lemmatization¶

Combining the sentiment with historical prices¶

LSTM Model¶

Linear Regression and Random Forests Models¶

Model Evaluation¶

	Date	Tweet	Stock Name	Company Name
0	2022-09-29 23:41:16+00:00	Mainstream media has done an amazing job at br...	TSLA	Tesla, Inc.
1	2022-09-29 23:24:43+00:00	Tesla delivery estimates are at around 364k fr...	TSLA	Tesla, Inc.
2	2022-09-29 23:18:08+00:00	3/ Even if I include 63.0M unvested RSUs as of...	TSLA	Tesla, Inc.
3	2022-09-29 22:40:07+00:00	@RealDanODowd @WholeMarsBlog @Tesla Hahaha why...	TSLA	Tesla, Inc.
4	2022-09-29 22:27:05+00:00	@RealDanODowd @Tesla Stop trying to kill kids,...	TSLA	Tesla, Inc.

	title	updated_time
0	Musk, Indonesian health minister, launch Starl...	2024-05-19
1	Mercedes workers in Alabama reject union, deal...	2024-05-18
2	UAW's stinging loss in Alabama won't kill nati...	2024-05-18
3	Canada exploring higher tariffs on Chinese EVs...	2024-05-17
4	Exclusive: Shanghai eases data-export curbs so...	2024-05-17

	title	updated_time
0	musk indonesian health minister launch starlin...	2024-05-19
1	mercedes worker alabama reject union deal setb...	2024-05-18
2	uaws stinging loss alabama wont kill national ...	2024-05-18
3	canada explore high tariff chinese evs bloombe...	2024-05-17
4	exclusive shanghai eas dataexport curb seek te...	2024-05-17
...	...	...
5454	tesla lose third value third time year	2024-04-29
5455	us stock wall street end sharply low tech sell...	2022-10-31
5456	global stock inch record high dollar weakens	2022-10-31
5457	stock oil rise asia us record dollar weakens	2022-10-31
5458	breakdown gamestop financial market whodunnit	2021-10-13

	Date	Cleaned Tweet	Tweet Sentiment	Tweet Sentiment Label
0	2022-09-29	mainstream medium do amazing job brainwash peo...	0.4019	Positive
1	2022-09-29	delivery estimate around k analyst	0.0000	Neutral
2	2022-09-29	even include unvested rsus additional equity n...	0.2960	Positive
3	2022-09-29	hahaha still try stop fsd bro get shit togethe...	-0.3400	Negative
4	2022-09-29	stop try kill kid sad derange old man	-0.8750	Negative
...	...	...	...	...
37408	2021-09-30	play dirt	0.0000	Neutral
37409	2021-09-30	agree ev business alone worth gt sh wo fsd lik...	0.6743	Positive
37410	2021-09-30	hold tight	0.0000	Neutral
37411	2021-09-30	get ready _ _ _ _ _ _ q delivery number ur answer	0.4215	Positive
37412	2021-09-30	word amd give preferential treatment bc know l...	0.4019	Positive

	id	created_at	full_text	score
0	1	2020-04-09 23:59:51+00:00	@KennyDegu very very little volume. With $10T ...	-0.7
1	2	2020-04-09 23:58:55+00:00	#ES_F achieved Target 2780 closing above 50% #...	0.0
2	3	2020-04-09 23:58:52+00:00	RT @KimbleCharting: Silver/Gold indicator crea...	-0.2
3	4	2020-04-09 23:58:27+00:00	@Issaquahfunds Hedged our $MSFT position into ...	-0.4
4	5	2020-04-09 23:57:59+00:00	RT @zipillinois: 3 Surprisingly Controversial ...	0.1

	Date	Cleaned Tweet	Custom Tweets Sentiment Scores	Custom Tweets Sentiment Labels
0	2022-09-29	mainstream medium do amazing job brainwash peo...	0.683358	positive
1	2022-09-29	delivery estimate around k analyst	-0.094422	negative
2	2022-09-29	even include unvested rsus additional equity n...	1.167162	positive
3	2022-09-29	hahaha still try stop fsd bro get shit togethe...	-0.705485	negative
4	2022-09-29	stop try kill kid sad derange old man	-0.800493	negative
...	...	...	...	...
37408	2021-09-30	play dirt	0.247036	positive
37409	2021-09-30	agree ev business alone worth gt sh wo fsd lik...	-0.248053	negative
37410	2021-09-30	hold tight	0.157599	positive
37411	2021-09-30	get ready _ _ _ _ _ _ q delivery number ur answer	0.961607	positive
37412	2021-09-30	word amd give preferential treatment bc know l...	0.092445	positive

	published_at	title	description	url	sentiment	sentiment_score
0	2021-10-21T14:40:39+00:00	Exclusive: Industrialist Nikhil Merchant leads...	Low-profile Gujarat businessman wants to add P...	https://www.businesstoday.in/latest/corporate/...	neutral	0.711336
1	2021-10-21T13:52:13+00:00	India’s Reliance gets shareholders’ nod to add...	BENGALURU — India’s Reliance Indus...	https://financialpost.com/pmn/business-pmn/ind...	neutral	0.800176
2	2021-10-21T12:45:52+00:00	Rogers misses quarterly revenue estimates	Rogers Communications Inc reported third-quart...	https://torontosun.com/business/money-news/rog...	negative	0.973185
3	2021-10-21T12:40:35+00:00	Exclusive: Tycoon Nikhil Merchant leads race f...	Low-profile Gujarat businessman wants to add P...	https://www.businesstoday.in/latest/corporate/...	neutral	0.619371
4	2021-10-21T11:43:53+00:00	BP to Open Fuel Station in India Amid Record P...	BP and Reliance Industries signed a $6 billion...	https://sputniknews.com/20211021/bp-to-open-fu...	positive	0.929962

	Date	News	Custom News Sentiment Scores	Custom News Sentiment Labels
0	2024-05-19	musk indonesian health minister launch starlin...	0.813590	positive
1	2024-05-18	mercedes worker alabama reject union deal setb...	0.767116	positive
2	2024-05-18	uaws stinging loss alabama wont kill national ...	0.872910	positive
3	2024-05-17	canada explore high tariff chinese evs bloombe...	0.786645	positive
4	2024-05-17	exclusive shanghai eas dataexport curb seek te...	0.809108	positive
...	...	...	...	...
5440	2024-04-29	tesla lose third value third time year	0.737230	positive
5441	2022-10-31	us stock wall street end sharply low tech sell...	0.877909	positive
5442	2022-10-31	global stock inch record high dollar weakens	0.720760	positive
5443	2022-10-31	stock oil rise asia us record dollar weakens	0.816167	positive
5444	2021-10-13	breakdown gamestop financial market whodunnit	0.870781	positive

	TB Tweets Sentiment	TB News Sentiment	Overall Score
Date
2021-09-30	0.110358	0.100000	0.105179
2021-10-01	0.128874	-0.003077	0.062899
2021-10-02	0.145486	0.000000	0.072743
2021-10-03	0.149520	NaN	0.149520
2021-10-04	0.069523	0.135052	0.102288

	Date	Adj Close	Daily Return	Cumulative Return	Trading Signal
813	2023-10-05	78609.390528	NaN	NaN	Sell
814	2023-10-06	78754.291446	0.001843	0.001843	Buy
815	2023-10-07	78754.291446	0.000000	0.001843	Sell
816	2023-10-09	78494.687665	-0.003296	-0.001453	Sell
817	2023-10-10	79687.068649	0.015191	0.013738	Buy
...	...	...	...	...	...
1006	2024-05-15	52630.463435	-0.020010	-0.320141	Sell
1007	2024-05-16	52887.050173	0.004875	-0.315266	Buy
1008	2024-05-17	53677.952713	0.014955	-0.300311	Buy
1009	2024-05-18	53677.952713	0.000000	-0.300311	Sell
1010	2024-05-19	53677.952713	0.000000	-0.300311	Sell

	Date	Adj Close	Daily Return	Cumulative Return	Trading Signal
813	2023-10-05	78609.390528	NaN	NaN	Stop Loss
814	2023-10-06	78754.291446	0.001843	0.001843	Buy
815	2023-10-07	78754.291446	0.000000	0.001843	Sell
816	2023-10-09	78494.687665	-0.003296	-0.001453	Sell
817	2023-10-10	79687.068649	0.015191	0.013738	Buy
...	...	...	...	...	...
1006	2024-05-15	52630.463435	-0.020010	-0.320141	Stop Loss
1007	2024-05-16	52887.050173	0.004875	-0.315266	Stop Loss
1008	2024-05-17	53677.952713	0.014955	-0.300311	Stop Loss
1009	2024-05-18	53677.952713	0.000000	-0.300311	Stop Loss
1010	2024-05-19	53677.952713	0.000000	-0.300311	Stop Loss